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class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20484143/On_Cutting_Force_Coefficient_Model_with_Respect_to_Tool_Geometry_and_Tool_Wear"><img alt="Research paper thumbnail of On Cutting Force Coefficient Model with Respect to Tool Geometry and Tool Wear" class="work-thumbnail" src="https://attachments.academia-assets.com/45111639/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20484143/On_Cutting_Force_Coefficient_Model_with_Respect_to_Tool_Geometry_and_Tool_Wear">On Cutting Force Coefficient Model with Respect to Tool Geometry and Tool Wear</a></div><div class="wp-workCard_item"><span>Procedia Manufacturing</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">EVWUDFW &XWWLQJ IRUFH PRGHOV DUH LPSRUWDQW IRU PDFKLQLQJ SURFHVVHV VLPXODWLRQV 7KLV SDSHU SUHVHQW...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">EVWUDFW &XWWLQJ IRUFH PRGHOV DUH LPSRUWDQW IRU PDFKLQLQJ SURFHVVHV VLPXODWLRQV 7KLV SDSHU SUHVHQWV PHDVXUHPHQW RI FXWWLQJ IRUFHV GXULQJ & FDUERQ VWHHO PDFKLQLQJ XVLQJ D FRDWHG FDUELGH WRRO 9DULRXV FXWWLQJ HGJH JHRPHWULHV DQG FXWWLQJ FRQGLWLRQV DV ZHOO DV YDULRXV WRRO IODQN ZHDU YDOXHV ZHUH WHVWHG 7KH FXWWLQJ IRUFH FRHIILFLHQWV ZHUH FRPSXWHG IURP WKH H[SHULPHQWDO GDWD 7KH UHVXOWV VKRZHG WKDW WKH DEVROXWH YDOXH RI WKH FXWWLQJ IRUFH FRHIILFLHQWV GHSHQGHG RQ WKH FXWWLQJ HGJH JHRPHWU\ FXWWLQJ FRQGLWLRQV DQG WRRO ZHDU $GGLWLRQDOO\ WKH UHODWLYH LQFUHDVH LQ WKH FXWWLQJ IRUFH FRHIILFLHQWV GXULQJ WKH WRRO OLIHWLPH ZDV OLQHDU DQG LQGHSHQGHQW RI WKH FXWWLQJ HGJH JHRPHWULHV DQG FXWWLQJ FRQGLWLRQV 7KH FXWWLQJ IRUFH FRHIILFLHQW PRGHOV ZLWK DQG ZLWKRXW FURVV FRPSRQHQWV ZHUH LGHQWLILHG XVLQJ WKH OLQHDU UHJUHVVLRQ PHWKRG &RPSDULVRQ RI ERWK PRGHOV LV GLVFXVVHG LQ WKH SDSHU .H\ZRUGV &XWWLQJ HGJH JHRPHWU\ IODQN ZHDU FRDWHG FDUELGH WRRO FXWWLQJ IRUFH FRHIILFLHQWV OLQHDU UHJUHVVLRQ ,QWURGXFWLRQ &XUUHQW PDFKLQLQJ VLPXODWLRQV IRFXV RQ D FRPSUHKHQVLYH GHVFULSWLRQ RI WKH SURFHVV±ZRUNSLHFH±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rocedia Manufacturing Volume 1, 2015, Pages 708-720 43rd Proceedings of the North American Manufacturing Research Institution of SME <a href="http://www.sme.org/namrc" rel="nofollow">http://www.sme.org/namrc</a> Cutting Force Coefficient Model w.r.t. Tool Geometry and Tool Wear Kolar, Fojtu, and Schmitz Cutting Force Coefficient Model w.r.t. Tool Geometry and Tool Wear Kolar, Fojtu, and Schmitz 710 Cutting Force Coefficient Model w.r.t. Tool Geometry and Tool Wear Kolar, Fojtu, and Schmitz 711 IODQN LV QRW LPSRUWDQW +RZHYHU WKH IODQN ZHDU DOVR LQIOXHQFHV WKH ORFDO YDOXHV RI WKH UDNH DQJOH 7KHUHIRUH WKH IODQN ZHDU VHHPV WR EH DV LPSRUWDQW DV WRRO UDNH JHRPHWU\ )LJXUH 5HVLGXDO SORWV IRU DFWLYH FXWWLQJ IRUFH ) $ )LJXUH 6HQVLWLYLW\ RI DFWLYH IRUFH ) $ WR FXWWLQJ HGJH JHRPHWU\ DQG FXWWLQJ FRQGLWLRQV Cutting Force Coefficient Model w.r.t. Tool Geometry and Tool Wear Kolar, Fojtu, and Schmitz 712 Cutting Force Coefficient Model w.r.t. Tool Geometry and Tool Wear Kolar, Fojtu, and Schmitz 717 Cutting Force Coefficient Model w.r.t. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484142-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484141"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20484141/The_Microphone_Feedback_Analogy_for_Chatter_in_Machining"><img alt="Research paper thumbnail of The Microphone Feedback Analogy for Chatter in Machining" class="work-thumbnail" src="https://attachments.academia-assets.com/45111591/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20484141/The_Microphone_Feedback_Analogy_for_Chatter_in_Machining">The Microphone Feedback Analogy for Chatter in Machining</a></div><div class="wp-workCard_item"><span>Shock and Vibration</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper provides experimental evidence for the analogy between the time-delay feedback in publ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper provides experimental evidence for the analogy between the time-delay feedback in public address systems and chatter in machining. Machining stability theory derived using the Nyquist criterion is applied to predict the squeal frequency in a microphone/speaker setup. Comparisons between predictions and measurements are presented.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1f92d27da3456656aef0190871c63140" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45111591,"asset_id":20484141,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45111591/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484141"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484141"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484141; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484141]").text(description); $(".js-view-count[data-work-id=20484141]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484141; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484141']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "1f92d27da3456656aef0190871c63140" } } $('.js-work-strip[data-work-id=20484141]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484141,"title":"The Microphone Feedback Analogy for Chatter in Machining","translated_title":"","metadata":{"ai_title_tag":"Microphone Feedback Analogy for Machining Chatter","grobid_abstract":"This paper provides experimental evidence for the analogy between the time-delay feedback in public address systems and chatter in machining. 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The temperature estimates are used to determine activation energy for thermally activated chemical wear of a PCD tool milling Monel 400 (Cu 32, Ni 65 wt%). Activation energy estimates are from 15 to 60 kJ / mole, depending on systematic uncertainties, such as diamond thermal conductivity, that still need to be addressed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="efa0ce3f19cf3aa4947266faa2b58158" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45111653,"asset_id":20484140,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45111653/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484140"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484140"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484140; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484140]").text(description); $(".js-view-count[data-work-id=20484140]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484140; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484140']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "efa0ce3f19cf3aa4947266faa2b58158" } } $('.js-work-strip[data-work-id=20484140]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484140,"title":"Estimation of Cutting Conditions in Precision Micromachining of CuNi Alloys of Varying Composition","translated_title":"","metadata":{"ai_title_tag":"Micromachining CuNi Alloys: Cutting Condition Model","grobid_abstract":"A non-dimensional model is developed that relates cutting edge temperatures to process inputs (speed and feed) and outputs (cutting and thrust forces) as well as tool and work thermal properties in micromachining with diamond tools. The temperature estimates are used to determine activation energy for thermally activated chemical wear of a PCD tool milling Monel 400 (Cu 32, Ni 65 wt%). 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In this approach, the optical interference signal is recorded during constant velocity target motion using a spectrum analyzer and the magnitudes of the individual periodic error contributors are used to calculate error magnitudes. This study builds on prior work by treating the general case where both first and second order error components exist and arbitrary initial phase values are considered. Significant experimental results are presented which verify the new approach.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8e71a55146d2709b67eeea423b10806d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41397779,"asset_id":20484138,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41397779/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484138"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484138"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484138; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484138]").text(description); $(".js-view-count[data-work-id=20484138]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484138; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484138']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8e71a55146d2709b67eeea423b10806d" } } $('.js-work-strip[data-work-id=20484138]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484138,"title":"Periodic error calculation from spectrum analyzer data","translated_title":"","metadata":{"ai_title_tag":"Monte Carlo Analysis of Periodic Errors","grobid_abstract":"This paper describes the Monte Carlo evaluation of a single equation that can be used to determine periodic error magnitudes from spectrum analyzer data. In this approach, the optical interference signal is recorded during constant velocity target motion using a spectrum analyzer and the magnitudes of the individual periodic error contributors are used to calculate error magnitudes. This study builds on prior work by treating the general case where both first and second order error components exist and arbitrary initial phase values are considered. Significant experimental results are presented which verify the new approach.","publication_date":{"day":null,"month":null,"year":2010,"errors":{}},"publication_name":"Precision Engineering","grobid_abstract_attachment_id":41397779},"translated_abstract":null,"internal_url":"https://www.academia.edu/20484138/Periodic_error_calculation_from_spectrum_analyzer_data","translated_internal_url":"","created_at":"2016-01-22T00:40:49.404-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41444068,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":41397779,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41397779/thumbnails/1.jpg","file_name":"Periodic_error_calculation_from_spectrum20160122-17374-cljksa.pdf","download_url":"https://www.academia.edu/attachments/41397779/download_file","bulk_download_file_name":"Periodic_error_calculation_from_spectrum.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41397779/Periodic_error_calculation_from_spectrum20160122-17374-cljksa-libre.pdf?1453452252=\u0026response-content-disposition=attachment%3B+filename%3DPeriodic_error_calculation_from_spectrum.pdf\u0026Expires=1743195852\u0026Signature=AIND15CFT6NyNw7LUBZ59mqpsHcfjSU0xz0ooEwQ25LbC8TpGZ5J9eB1GdRDeAO316~NA3zF4A28LZ7U8WRELkjMLXXBgfnsWXRt3ZRgyZwkZXO9QgtbiHnHuRrxaQvcKeAubDQXzJ7ybdGN3UNm6D4pmSafT982ZlD5sjMJiXrIcYCkg6vTxaUVPuAvvgDnOnXIE2140gKApoZwtab7QU5k5htj4f5mvo~O16tzF-PE928H~PHTeJ2t-c2A3YYsSgLHSriDnZ-7gDtUJBYjM2~ANzlZL-19-r78w375U9uB1MRLE0ggqFcWqODbeyDhHsSRMu3vX~rTgLZQPELz7g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Periodic_error_calculation_from_spectrum_analyzer_data","translated_slug":"","page_count":13,"language":"en","content_type":"Work","summary":"This paper describes the Monte Carlo evaluation of a single equation that can be used to determine periodic error magnitudes from spectrum analyzer data. In this approach, the optical interference signal is recorded during constant velocity target motion using a spectrum analyzer and the magnitudes of the individual periodic error contributors are used to calculate error magnitudes. This study builds on prior work by treating the general case where both first and second order error components exist and arbitrary initial phase values are considered. Significant experimental results are presented which verify the new approach.","owner":{"id":41444068,"first_name":"Tony","middle_initials":null,"last_name":"Schmitz","page_name":"TonySchmitz","domain_name":"uncc","created_at":"2016-01-13T18:28:23.678-08:00","display_name":"Tony Schmitz","url":"https://uncc.academia.edu/TonySchmitz"},"attachments":[{"id":41397779,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41397779/thumbnails/1.jpg","file_name":"Periodic_error_calculation_from_spectrum20160122-17374-cljksa.pdf","download_url":"https://www.academia.edu/attachments/41397779/download_file","bulk_download_file_name":"Periodic_error_calculation_from_spectrum.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41397779/Periodic_error_calculation_from_spectrum20160122-17374-cljksa-libre.pdf?1453452252=\u0026response-content-disposition=attachment%3B+filename%3DPeriodic_error_calculation_from_spectrum.pdf\u0026Expires=1743195852\u0026Signature=AIND15CFT6NyNw7LUBZ59mqpsHcfjSU0xz0ooEwQ25LbC8TpGZ5J9eB1GdRDeAO316~NA3zF4A28LZ7U8WRELkjMLXXBgfnsWXRt3ZRgyZwkZXO9QgtbiHnHuRrxaQvcKeAubDQXzJ7ybdGN3UNm6D4pmSafT982ZlD5sjMJiXrIcYCkg6vTxaUVPuAvvgDnOnXIE2140gKApoZwtab7QU5k5htj4f5mvo~O16tzF-PE928H~PHTeJ2t-c2A3YYsSgLHSriDnZ-7gDtUJBYjM2~ANzlZL-19-r78w375U9uB1MRLE0ggqFcWqODbeyDhHsSRMu3vX~rTgLZQPELz7g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":923,"name":"Technology","url":"https://www.academia.edu/Documents/in/Technology"},{"id":118582,"name":"Physical sciences","url":"https://www.academia.edu/Documents/in/Physical_sciences"},{"id":1286358,"name":"Precision Engineering","url":"https://www.academia.edu/Documents/in/Precision_Engineering"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484138-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484135"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20484135/Sensor_design_and_evaluation_for_on_machine_probing_of_extruded_tool_joints"><img alt="Research paper thumbnail of Sensor design and evaluation for on-machine probing of extruded tool joints" class="work-thumbnail" src="https://attachments.academia-assets.com/45111704/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20484135/Sensor_design_and_evaluation_for_on_machine_probing_of_extruded_tool_joints">Sensor design and evaluation for on-machine probing of extruded tool joints</a></div><div class="wp-workCard_item"><span>Precision Engineering</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT This paper describes the design and evaluation of two contact probes used to measure the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT This paper describes the design and evaluation of two contact probes used to measure the length and bore concentricity of cylindrical, extruded tool joints while clamped in a production lathe spindle. The probes consisted of an LVDT, a spring-preloaded shaft supported by linear bearings used to isolate the LVDT from side loads, and a hardened steel sphere to contact the rough surface. For bore concentricity measurements, a parallelogram leaf-type flexure and 45° surface was used to transfer radial deviations to the spindle/part/LVDT axis. The LVDT output was used in conjunction with the lathe turret position to determine the extruded part dimensions prior to machining. Experimental results are provided for measurements of multiple parts; variations in length, internal diameter, and bore concentricity are compared to the nominal dimensions. Additionally, a calibration artifact is described which enabled evaluation of the measurement accuracies for the two probes. Given the pre-machining part dimensions, it is shown how this information can be used to select from a pre-defined matrix of part programs to reduce cycle time and machining cost.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9d119679a5546ce784b28e3c3736eba9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45111704,"asset_id":20484135,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45111704/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484135"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484135"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484135; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484135]").text(description); $(".js-view-count[data-work-id=20484135]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484135; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484135']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9d119679a5546ce784b28e3c3736eba9" } } $('.js-work-strip[data-work-id=20484135]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484135,"title":"Sensor design and evaluation for on-machine probing of extruded tool joints","translated_title":"","metadata":{"abstract":"ABSTRACT This paper describes the design and evaluation of two contact probes used to measure the length and bore concentricity of cylindrical, extruded tool joints while clamped in a production lathe spindle. The probes consisted of an LVDT, a spring-preloaded shaft supported by linear bearings used to isolate the LVDT from side loads, and a hardened steel sphere to contact the rough surface. For bore concentricity measurements, a parallelogram leaf-type flexure and 45° surface was used to transfer radial deviations to the spindle/part/LVDT axis. The LVDT output was used in conjunction with the lathe turret position to determine the extruded part dimensions prior to machining. Experimental results are provided for measurements of multiple parts; variations in length, internal diameter, and bore concentricity are compared to the nominal dimensions. Additionally, a calibration artifact is described which enabled evaluation of the measurement accuracies for the two probes. Given the pre-machining part dimensions, it is shown how this information can be used to select from a pre-defined matrix of part programs to reduce cycle time and machining cost.","publication_date":{"day":null,"month":null,"year":2011,"errors":{}},"publication_name":"Precision Engineering"},"translated_abstract":"ABSTRACT This paper describes the design and evaluation of two contact probes used to measure the length and bore concentricity of cylindrical, extruded tool joints while clamped in a production lathe spindle. The probes consisted of an LVDT, a spring-preloaded shaft supported by linear bearings used to isolate the LVDT from side loads, and a hardened steel sphere to contact the rough surface. For bore concentricity measurements, a parallelogram leaf-type flexure and 45° surface was used to transfer radial deviations to the spindle/part/LVDT axis. The LVDT output was used in conjunction with the lathe turret position to determine the extruded part dimensions prior to machining. Experimental results are provided for measurements of multiple parts; variations in length, internal diameter, and bore concentricity are compared to the nominal dimensions. Additionally, a calibration artifact is described which enabled evaluation of the measurement accuracies for the two probes. Given the pre-machining part dimensions, it is shown how this information can be used to select from a pre-defined matrix of part programs to reduce cycle time and machining cost.","internal_url":"https://www.academia.edu/20484135/Sensor_design_and_evaluation_for_on_machine_probing_of_extruded_tool_joints","translated_internal_url":"","created_at":"2016-01-22T00:40:49.218-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41444068,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":45111704,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/45111704/thumbnails/1.jpg","file_name":"on_machine_probing.pdf","download_url":"https://www.academia.edu/attachments/45111704/download_file","bulk_download_file_name":"Sensor_design_and_evaluation_for_on_mach.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/45111704/on_machine_probing-libre.pdf?1461703982=\u0026response-content-disposition=attachment%3B+filename%3DSensor_design_and_evaluation_for_on_mach.pdf\u0026Expires=1743195852\u0026Signature=HGZkCexpVAWRDybXEKAUlHen1piN-yhre44voYXlx6LsDWYELm-aUQzcwwDRGK9E-qOr6HczAOVGk0jX485B3cIAJlhWHL-Ud~Dig8YTHp229m1oVVQ10kLXor9dqIpXkqJ49sJL8LocJn-czQw~v0Uh0cDogM3WVH9WDeU6GmvmvShAdljV2qttkuVjhuBVpOMVvsE8dDJp~wk2Z6AST6fIVeQPfUIAwxYW1n~aakO~cf8nIelwPz6~4patd0QpWSd~SatM3nm3fQ949ZXFYOGDcWWeYrgdq2~c-1tneE9SoA6ycgCNVFQIhAOh69NtyfXenGireSfiLhF5Itsthg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Sensor_design_and_evaluation_for_on_machine_probing_of_extruded_tool_joints","translated_slug":"","page_count":11,"language":"en","content_type":"Work","summary":"ABSTRACT This paper describes the design and evaluation of two contact probes used to measure the length and bore concentricity of cylindrical, extruded tool joints while clamped in a production lathe spindle. The probes consisted of an LVDT, a spring-preloaded shaft supported by linear bearings used to isolate the LVDT from side loads, and a hardened steel sphere to contact the rough surface. For bore concentricity measurements, a parallelogram leaf-type flexure and 45° surface was used to transfer radial deviations to the spindle/part/LVDT axis. The LVDT output was used in conjunction with the lathe turret position to determine the extruded part dimensions prior to machining. Experimental results are provided for measurements of multiple parts; variations in length, internal diameter, and bore concentricity are compared to the nominal dimensions. Additionally, a calibration artifact is described which enabled evaluation of the measurement accuracies for the two probes. Given the pre-machining part dimensions, it is shown how this information can be used to select from a pre-defined matrix of part programs to reduce cycle time and machining cost.","owner":{"id":41444068,"first_name":"Tony","middle_initials":null,"last_name":"Schmitz","page_name":"TonySchmitz","domain_name":"uncc","created_at":"2016-01-13T18:28:23.678-08:00","display_name":"Tony Schmitz","url":"https://uncc.academia.edu/TonySchmitz"},"attachments":[{"id":45111704,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/45111704/thumbnails/1.jpg","file_name":"on_machine_probing.pdf","download_url":"https://www.academia.edu/attachments/45111704/download_file","bulk_download_file_name":"Sensor_design_and_evaluation_for_on_mach.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/45111704/on_machine_probing-libre.pdf?1461703982=\u0026response-content-disposition=attachment%3B+filename%3DSensor_design_and_evaluation_for_on_mach.pdf\u0026Expires=1743195852\u0026Signature=HGZkCexpVAWRDybXEKAUlHen1piN-yhre44voYXlx6LsDWYELm-aUQzcwwDRGK9E-qOr6HczAOVGk0jX485B3cIAJlhWHL-Ud~Dig8YTHp229m1oVVQ10kLXor9dqIpXkqJ49sJL8LocJn-czQw~v0Uh0cDogM3WVH9WDeU6GmvmvShAdljV2qttkuVjhuBVpOMVvsE8dDJp~wk2Z6AST6fIVeQPfUIAwxYW1n~aakO~cf8nIelwPz6~4patd0QpWSd~SatM3nm3fQ949ZXFYOGDcWWeYrgdq2~c-1tneE9SoA6ycgCNVFQIhAOh69NtyfXenGireSfiLhF5Itsthg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":923,"name":"Technology","url":"https://www.academia.edu/Documents/in/Technology"},{"id":118582,"name":"Physical sciences","url":"https://www.academia.edu/Documents/in/Physical_sciences"},{"id":1286358,"name":"Precision Engineering","url":"https://www.academia.edu/Documents/in/Precision_Engineering"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484135-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484132"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20484132/Chatter_recognition_by_a_statistical_evaluation_of_the_synchronously_sampled_audio_signal"><img alt="Research paper thumbnail of Chatter recognition by a statistical evaluation of the synchronously sampled audio signal" class="work-thumbnail" src="https://attachments.academia-assets.com/45111870/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20484132/Chatter_recognition_by_a_statistical_evaluation_of_the_synchronously_sampled_audio_signal">Chatter recognition by a statistical evaluation of the synchronously sampled audio signal</a></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-20484132-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-20484132-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666572/figure-1-fifty-per-cent-radial-immersion-milling-schematic"><img alt="Fig. 1. Fifty per cent radial immersion milling schematic; cutter rotation is clockwise. T.L. Schmitz | Journal of Sound and Vibration 262 (2003) 721-730 " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666590/figure-2-example-stability-lobe-diagram-showing-separation"><img alt="Fig. 2. Example stability lobe diagram showing separation between stable and unstable cutting conditions as a function of allowable chip width and spindle speed. " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666608/figure-3-fifty-per-cent-radial-immersion-scutting-example"><img alt="Fig. 3. Fifty per cent radial immersion stable cutting example. The notion of a statistical evaluation of the once-per-revolution milling audio signal to detec 1atter is based on Poincaré mapping techniques, where a local description of transient behavior constructed from the Poincaré map and the system stability may be established [17]. For milling le stability can be evaluated by plotting the x direction versus y direction tool motions and lentifying the once-per-revolution sampled data points [18]. For stable cutting, the mchronously sampled points approach a fixed point for the Poincaré map after some initia ansients and, thus, provide a tight distribution. Physically, this means that, although the tool is brating in the two orthogonal directions, the motions are synchronous with spindle rotation and le tool is returning to approximately the same position in each revolution under steady state ynditions. In contrast, tool motions during regenerative chatter are not synchronous with spindle tation; instead, they occur near the natural frequency corresponding to the most flexible system ode due to the nature of self-excited vibrations. For these unstable cuts, the tool does not return ) the same position each revolution. Rather, the once-per-revolution sampled distribution can nd toward an elliptical shape due to the quasi-periodic nature of chatter. The two cases are 10wn in Figs. 3 and 4. In both instances, simulated x and y direction tool motions, as well as the oe " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666627/figure-5-histogram-of-once-per-revolution-points-for-radial"><img alt="Fig. 5. Histogram of once-per-revolution points for stable 50% radial immersion cut. Fig. 4. Fifty per cent radial immersion unstable cutting example. " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666648/figure-5-schmitz-journal-of-sound-and-vibration"><img alt="T.L. Schmitz | Journal of Sound and Vibration 262 (2003) 721-730 " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666671/figure-6-histogram-of-once-per-revolution-points-for-uns"><img alt="Fig. 6. Histogram of once-per-revolution points for unstable 50% radial immersion cut. T.L. Schmitz | Journal of Sound and Vibration 262 (2003) 721-730 " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_006.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666688/figure-7-set-up-for-cutting-tests-showing-microphone-and"><img alt="Fig. 7. Set-up for cutting tests showing microphone and infrared emitter/detector pair. T.L. Schmitz | Journal of Sound and Vibration 262 (2003) 721-730 " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_007.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666706/figure-8-variance-values-in-mv-for-cutting-tests-using"><img alt="Fig. 8. Variance values in mV for cutting tests using emitter/detector pair once-per-revolution trigger. T.L. Schmitz | Journal of Sound and Vibration 262 (2003) 721-730 " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_008.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666723/figure-9-variance-values-in-mv-for-cutting-tests-using"><img alt="Fig. 9. Variance values in mV~ for cutting tests using synthetic actual spindle speed once-per-revolution trigger. T.L. Schmitz | Journal of Sound and Vibration 262 (2003) 721-730 " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_009.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666741/figure-10-variance-values-in-mv-for-cutting-tests-using"><img alt="Fig. 10. Variance values in mV? for cutting tests using synthetic nominal spindle speed once-per-revolution trigger. T.L. Schmitz | Journal of Sound and Vibration 262 (2003) 721-730 variance values for a number of stable and unstable cuts was completed for various strategies in obtaining the once-per-revolution trigger, which included: (1) obtaining the once-per-revolution signal using a locally derived trigger; (2) generating a synthetic once-per-revolution sampling trigger using the actual spindle speed; and (3) producing the once-per-revolution signal from the nominal (commanded) spindle speed. In all cases, the calculated variance values detected the transition from stable to unstable cutting. 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The method leverages the variance in cutting data, reducing the need for extensive signal processing typically required in frequency domain analysis. A preliminary system, the Harmonizert, is outlined for its functionality in real-time monitoring of milling stability, targeting practical implementation in shop floor conditions with limited engineering support.","ai_title_tag":"Statistical Chatter Recognition in Milling"},"translated_abstract":null,"internal_url":"https://www.academia.edu/20484132/Chatter_recognition_by_a_statistical_evaluation_of_the_synchronously_sampled_audio_signal","translated_internal_url":"","created_at":"2016-01-22T00:40:48.888-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41444068,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":45111870,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/45111870/thumbnails/1.jpg","file_name":"chatter_statistical.pdf","download_url":"https://www.academia.edu/attachments/45111870/download_file","bulk_download_file_name":"Chatter_recognition_by_a_statistical_eva.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/45111870/chatter_statistical-libre.pdf?1461704420=\u0026response-content-disposition=attachment%3B+filename%3DChatter_recognition_by_a_statistical_eva.pdf\u0026Expires=1743195852\u0026Signature=J765YTQfouvjfVV5k3LC8ZnOu8DaMyMFLvlKQENixKVBWAP-IMfSC6yLXK5qqLc8Cweekgv0MYwVAVKJZAs9puuu~y7DlGZX56J~xDNXh2UPDcOh8J2rSaKtJuMxjjAN0UG2StpTbnU2GyR3sICTYyglBpqc1FuqWG6qRbsHulhh~BrtCvoV~jaraXSG5Xzls~OZCNnbPxhTZtt2~9JKAe59ZNpg15A0Z0ZveRy4DRKw7zPFlMPBBq2zn2Gfzmetbpc6hHxGnYCeRLHC3ucGchobLj7KKpKB5YoiQwVnNh58LMFa7FcVTVBXvsUzG5B0CqGhRLqrJpYsVVbkxtJDHQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Chatter_recognition_by_a_statistical_evaluation_of_the_synchronously_sampled_audio_signal","translated_slug":"","page_count":10,"language":"en","content_type":"Work","summary":null,"owner":{"id":41444068,"first_name":"Tony","middle_initials":null,"last_name":"Schmitz","page_name":"TonySchmitz","domain_name":"uncc","created_at":"2016-01-13T18:28:23.678-08:00","display_name":"Tony Schmitz","url":"https://uncc.academia.edu/TonySchmitz"},"attachments":[{"id":45111870,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/45111870/thumbnails/1.jpg","file_name":"chatter_statistical.pdf","download_url":"https://www.academia.edu/attachments/45111870/download_file","bulk_download_file_name":"Chatter_recognition_by_a_statistical_eva.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/45111870/chatter_statistical-libre.pdf?1461704420=\u0026response-content-disposition=attachment%3B+filename%3DChatter_recognition_by_a_statistical_eva.pdf\u0026Expires=1743195852\u0026Signature=J765YTQfouvjfVV5k3LC8ZnOu8DaMyMFLvlKQENixKVBWAP-IMfSC6yLXK5qqLc8Cweekgv0MYwVAVKJZAs9puuu~y7DlGZX56J~xDNXh2UPDcOh8J2rSaKtJuMxjjAN0UG2StpTbnU2GyR3sICTYyglBpqc1FuqWG6qRbsHulhh~BrtCvoV~jaraXSG5Xzls~OZCNnbPxhTZtt2~9JKAe59ZNpg15A0Z0ZveRy4DRKw7zPFlMPBBq2zn2Gfzmetbpc6hHxGnYCeRLHC3ucGchobLj7KKpKB5YoiQwVnNh58LMFa7FcVTVBXvsUzG5B0CqGhRLqrJpYsVVbkxtJDHQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[{"id":6208465,"url":"http://highspeedmachining.mae.ufl.edu/htmlsite/publications/dr_schmitz/chatter_synchronous.pdf"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484112-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484111"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20484111/In_situ_monitoring_and_prediction_of_progressive_joint_wear_using_Bayesian_statistics"><img alt="Research paper thumbnail of In situ monitoring and prediction of progressive joint wear using Bayesian statistics" class="work-thumbnail" src="https://attachments.academia-assets.com/41397737/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20484111/In_situ_monitoring_and_prediction_of_progressive_joint_wear_using_Bayesian_statistics">In situ monitoring and prediction of progressive joint wear using Bayesian statistics</a></div><div class="wp-workCard_item"><span>Wear</span><span>, May 1, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, a statistical methodology of estimating wear coefficient and predicting wear volum...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, a statistical methodology of estimating wear coefficient and predicting wear volume in a revolute joint using in situ measurement data is presented. An instrumented slider-crank mechanism that can measure the joint force and the relative motion between the pin and bushing is built. The former is measured using a load cell built onto a necked portion of the hollow steel pin, while the latter is measured using a capacitance probe. In order to isolate the effect of friction in other joints, a porous carbon air bearing for the revolute joint between the follower link and the slide stage, as well as a prismatic joint for the linear slide, are used. Based on the relative motion between the centers of the pin and bushing, the wear volumes are estimated at six different operating cycles. The Bayesian inference technique is used to update the distribution of wear coefficients, which incorporates in situ measurement data to obtain the posterior distribution. The Markov Chain Monte Carlo technique is employed to generate samples from the given distribution. The results show that it is possible to narrow the distribution of wear coefficients and to predict the future wear volume with reasonable confidence. The effect of the prior distribution on the wear coefficient is discussed by comparing with the non-informative case.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="28675567ebc59647a37f24370e1347f5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41397737,"asset_id":20484111,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41397737/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484111"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484111"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484111; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484111]").text(description); $(".js-view-count[data-work-id=20484111]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484111; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484111']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "28675567ebc59647a37f24370e1347f5" } } $('.js-work-strip[data-work-id=20484111]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484111,"title":"In situ monitoring and prediction of progressive joint wear using Bayesian statistics","translated_title":"","metadata":{"ai_title_tag":"Bayesian In Situ Joint Wear Monitoring and Prediction","grobid_abstract":"In this paper, a statistical methodology of estimating wear coefficient and predicting wear volume in a revolute joint using in situ measurement data is presented. An instrumented slider-crank mechanism that can measure the joint force and the relative motion between the pin and bushing is built. The former is measured using a load cell built onto a necked portion of the hollow steel pin, while the latter is measured using a capacitance probe. In order to isolate the effect of friction in other joints, a porous carbon air bearing for the revolute joint between the follower link and the slide stage, as well as a prismatic joint for the linear slide, are used. Based on the relative motion between the centers of the pin and bushing, the wear volumes are estimated at six different operating cycles. The Bayesian inference technique is used to update the distribution of wear coefficients, which incorporates in situ measurement data to obtain the posterior distribution. The Markov Chain Monte Carlo technique is employed to generate samples from the given distribution. The results show that it is possible to narrow the distribution of wear coefficients and to predict the future wear volume with reasonable confidence. The effect of the prior distribution on the wear coefficient is discussed by comparing with the non-informative case.","publication_date":{"day":1,"month":5,"year":2011,"errors":{}},"publication_name":"Wear","grobid_abstract_attachment_id":41397737},"translated_abstract":null,"internal_url":"https://www.academia.edu/20484111/In_situ_monitoring_and_prediction_of_progressive_joint_wear_using_Bayesian_statistics","translated_internal_url":"","created_at":"2016-01-22T00:40:18.657-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41444068,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":41397737,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41397737/thumbnails/1.jpg","file_name":"In-situ_monitoring_and_prediction_of_pro20160122-7958-uejvlz.pdf","download_url":"https://www.academia.edu/attachments/41397737/download_file","bulk_download_file_name":"In_situ_monitoring_and_prediction_of_pro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41397737/In-situ_monitoring_and_prediction_of_pro20160122-7958-uejvlz-libre.pdf?1453452219=\u0026response-content-disposition=attachment%3B+filename%3DIn_situ_monitoring_and_prediction_of_pro.pdf\u0026Expires=1743195852\u0026Signature=V6IYr4ymG1WOGHTKOPMfl---6RQ9udPL8MNoSmuMzIqC7b5-~ph33bXAmqz~waRHZJ5FtNguaHB2aAbzuh~HRK-oyO-CYSjRBm-6SvmIf2ru-TAfSAIEAe18BLDUdptmCVemOMNnue8voSZN248J46idnw6Ty6YSrZyMhLx36JoWBb-xXfhl4e84SduHkz7WZfEB-acpJSneIQlexXLY5wsyEA~aCg47vo~DsBUqjgFsSer5Jx5c5MLdd9e6SIYPcFrwHwqmVHqwg-9JERMh4BTF3b-XDEEKv5FfEUC3-LO5whnfgTMN9YdLX918lPdY89L7TVnuS~XvMytbI-DhJA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"In_situ_monitoring_and_prediction_of_progressive_joint_wear_using_Bayesian_statistics","translated_slug":"","page_count":12,"language":"en","content_type":"Work","summary":"In this paper, a statistical methodology of estimating wear coefficient and predicting wear volume in a revolute joint using in situ measurement data is presented. An instrumented slider-crank mechanism that can measure the joint force and the relative motion between the pin and bushing is built. The former is measured using a load cell built onto a necked portion of the hollow steel pin, while the latter is measured using a capacitance probe. In order to isolate the effect of friction in other joints, a porous carbon air bearing for the revolute joint between the follower link and the slide stage, as well as a prismatic joint for the linear slide, are used. Based on the relative motion between the centers of the pin and bushing, the wear volumes are estimated at six different operating cycles. The Bayesian inference technique is used to update the distribution of wear coefficients, which incorporates in situ measurement data to obtain the posterior distribution. The Markov Chain Monte Carlo technique is employed to generate samples from the given distribution. The results show that it is possible to narrow the distribution of wear coefficients and to predict the future wear volume with reasonable confidence. The effect of the prior distribution on the wear coefficient is discussed by comparing with the non-informative case.","owner":{"id":41444068,"first_name":"Tony","middle_initials":null,"last_name":"Schmitz","page_name":"TonySchmitz","domain_name":"uncc","created_at":"2016-01-13T18:28:23.678-08:00","display_name":"Tony Schmitz","url":"https://uncc.academia.edu/TonySchmitz"},"attachments":[{"id":41397737,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41397737/thumbnails/1.jpg","file_name":"In-situ_monitoring_and_prediction_of_pro20160122-7958-uejvlz.pdf","download_url":"https://www.academia.edu/attachments/41397737/download_file","bulk_download_file_name":"In_situ_monitoring_and_prediction_of_pro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41397737/In-situ_monitoring_and_prediction_of_pro20160122-7958-uejvlz-libre.pdf?1453452219=\u0026response-content-disposition=attachment%3B+filename%3DIn_situ_monitoring_and_prediction_of_pro.pdf\u0026Expires=1743195852\u0026Signature=V6IYr4ymG1WOGHTKOPMfl---6RQ9udPL8MNoSmuMzIqC7b5-~ph33bXAmqz~waRHZJ5FtNguaHB2aAbzuh~HRK-oyO-CYSjRBm-6SvmIf2ru-TAfSAIEAe18BLDUdptmCVemOMNnue8voSZN248J46idnw6Ty6YSrZyMhLx36JoWBb-xXfhl4e84SduHkz7WZfEB-acpJSneIQlexXLY5wsyEA~aCg47vo~DsBUqjgFsSer5Jx5c5MLdd9e6SIYPcFrwHwqmVHqwg-9JERMh4BTF3b-XDEEKv5FfEUC3-LO5whnfgTMN9YdLX918lPdY89L7TVnuS~XvMytbI-DhJA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":56,"name":"Materials Engineering","url":"https://www.academia.edu/Documents/in/Materials_Engineering"},{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering"},{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis"},{"id":6177,"name":"Modeling","url":"https://www.academia.edu/Documents/in/Modeling"},{"id":23008,"name":"Wear","url":"https://www.academia.edu/Documents/in/Wear"},{"id":23032,"name":"Tribology","url":"https://www.academia.edu/Documents/in/Tribology"},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference"},{"id":61603,"name":"Uncertainty","url":"https://www.academia.edu/Documents/in/Uncertainty"},{"id":85262,"name":"Markov Chain Monte Carlo","url":"https://www.academia.edu/Documents/in/Markov_Chain_Monte_Carlo"},{"id":100094,"name":"Bayesian statistics","url":"https://www.academia.edu/Documents/in/Bayesian_statistics"},{"id":389570,"name":"Capacitance","url":"https://www.academia.edu/Documents/in/Capacitance"},{"id":1333436,"name":"Monte Carlo Method","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Method"},{"id":1554438,"name":"Load Cell","url":"https://www.academia.edu/Documents/in/Load_Cell"}],"urls":[{"id":6208459,"url":"http://cat.inist.fr/?aModele=afficheN\u0026cpsidt=24155097"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484111-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484110"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20484110/TOOL_LIFE_PREDICTION_USING_RANDOM_WALK_BAYESIAN_UPDATING"><img alt="Research paper thumbnail of TOOL LIFE PREDICTION USING RANDOM WALK BAYESIAN UPDATING" class="work-thumbnail" src="https://attachments.academia-assets.com/45111949/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20484110/TOOL_LIFE_PREDICTION_USING_RANDOM_WALK_BAYESIAN_UPDATING">TOOL LIFE PREDICTION USING RANDOM WALK BAYESIAN UPDATING</a></div><div class="wp-workCard_item"><span>Http Dx Doi Org 10 1080 10910344 2013 806103</span><span>, Jul 3, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT According to the Taylor tool life equation, tool life reduces with increasing cutting sp...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT According to the Taylor tool life equation, tool life reduces with increasing cutting speed. The influence of additional factors can also be incorporated. However, tool wear is generally considered a stochastic process with uncertainty in the model constants. In this work, Bayesian inference is applied to predict tool life for milling/turning operations using the random walk/surface methods. For milling, Bayesian inference using a random walk approach is applied to the well-known Taylor tool life model. Tool wear tests are performed using an uncoated carbide tool and AISI 1018 steel work material. Test results are used to update the probability distribution of tool life. The updated beliefs are then applied to predict tool life using a probability distribution. For turning, both cutting speed and feed are considered. Bayesian updating is performed using the random surface technique. Turning tests are completed using a coated carbide tool and forged AISI 4137 chrome alloy steel. The test results are applied to update the probability distribution of tool life and the updated beliefs are used to predict tool life. While this work uses the Taylor model, by following the procedures described here, the technique can be applied to other tool life models as well.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ac57b23cefe0788caf6a2f8c9054de38" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45111949,"asset_id":20484110,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45111949/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484110"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484110"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484110; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484110]").text(description); $(".js-view-count[data-work-id=20484110]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484110; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484110']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ac57b23cefe0788caf6a2f8c9054de38" } } $('.js-work-strip[data-work-id=20484110]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484110,"title":"TOOL LIFE PREDICTION USING RANDOM WALK BAYESIAN UPDATING","translated_title":"","metadata":{"abstract":"ABSTRACT According to the Taylor tool life equation, tool life reduces with increasing cutting speed. The influence of additional factors can also be incorporated. However, tool wear is generally considered a stochastic process with uncertainty in the model constants. In this work, Bayesian inference is applied to predict tool life for milling/turning operations using the random walk/surface methods. For milling, Bayesian inference using a random walk approach is applied to the well-known Taylor tool life model. Tool wear tests are performed using an uncoated carbide tool and AISI 1018 steel work material. Test results are used to update the probability distribution of tool life. The updated beliefs are then applied to predict tool life using a probability distribution. For turning, both cutting speed and feed are considered. Bayesian updating is performed using the random surface technique. Turning tests are completed using a coated carbide tool and forged AISI 4137 chrome alloy steel. The test results are applied to update the probability distribution of tool life and the updated beliefs are used to predict tool life. While this work uses the Taylor model, by following the procedures described here, the technique can be applied to other tool life models as well.","ai_title_tag":"Bayesian Tool Life Prediction via Random Walk","publication_date":{"day":3,"month":7,"year":2013,"errors":{}},"publication_name":"Http Dx Doi Org 10 1080 10910344 2013 806103"},"translated_abstract":"ABSTRACT According to the Taylor tool life equation, tool life reduces with increasing cutting speed. The influence of additional factors can also be incorporated. However, tool wear is generally considered a stochastic process with uncertainty in the model constants. In this work, Bayesian inference is applied to predict tool life for milling/turning operations using the random walk/surface methods. For milling, Bayesian inference using a random walk approach is applied to the well-known Taylor tool life model. Tool wear tests are performed using an uncoated carbide tool and AISI 1018 steel work material. Test results are used to update the probability distribution of tool life. The updated beliefs are then applied to predict tool life using a probability distribution. For turning, both cutting speed and feed are considered. Bayesian updating is performed using the random surface technique. Turning tests are completed using a coated carbide tool and forged AISI 4137 chrome alloy steel. The test results are applied to update the probability distribution of tool life and the updated beliefs are used to predict tool life. While this work uses the Taylor model, by following the procedures described here, the technique can be applied to other tool life models as well.","internal_url":"https://www.academia.edu/20484110/TOOL_LIFE_PREDICTION_USING_RANDOM_WALK_BAYESIAN_UPDATING","translated_internal_url":"","created_at":"2016-01-22T00:40:18.429-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41444068,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":45111949,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/45111949/thumbnails/1.jpg","file_name":"tool_life_random_walk.pdf","download_url":"https://www.academia.edu/attachments/45111949/download_file","bulk_download_file_name":"TOOL_LIFE_PREDICTION_USING_RANDOM_WALK_B.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/45111949/tool_life_random_walk-libre.pdf?1461704816=\u0026response-content-disposition=attachment%3B+filename%3DTOOL_LIFE_PREDICTION_USING_RANDOM_WALK_B.pdf\u0026Expires=1743195852\u0026Signature=XFXi2tSTx46ZlblHdpQrKkWilonNX3ZZfJPhDBNP4u9daeV4PXFT6RUseMhc3Cl5GvO4tOANHMJOWAh11ShzVtGumoyBHFFk0AsDFE2O9j3I4RUlh1xa19sPDF9q2CqfYkI0uvNRIfZBarhbwAs-GqcqNNxI0MXTlPQarnRiOR-cMAml1qWRmoAFQfyif8wBh35ytDwh76CZUjTc0R7XgHPSwWCyw1mz~Ik5K5pIFiIwaFg3cXkDnxG9AzrLRSBkGkhuaGWJy01bcESo6E6-S74QkJdDy-DK2i52tUCnzINszR2-EETtxm3L9WoEgttxoGg10llD5AM9B9tZ6-FRQw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"TOOL_LIFE_PREDICTION_USING_RANDOM_WALK_BAYESIAN_UPDATING","translated_slug":"","page_count":34,"language":"en","content_type":"Work","summary":"ABSTRACT According to the Taylor tool life equation, tool life reduces with increasing cutting speed. The influence of additional factors can also be incorporated. However, tool wear is generally considered a stochastic process with uncertainty in the model constants. In this work, Bayesian inference is applied to predict tool life for milling/turning operations using the random walk/surface methods. For milling, Bayesian inference using a random walk approach is applied to the well-known Taylor tool life model. Tool wear tests are performed using an uncoated carbide tool and AISI 1018 steel work material. Test results are used to update the probability distribution of tool life. The updated beliefs are then applied to predict tool life using a probability distribution. For turning, both cutting speed and feed are considered. Bayesian updating is performed using the random surface technique. Turning tests are completed using a coated carbide tool and forged AISI 4137 chrome alloy steel. The test results are applied to update the probability distribution of tool life and the updated beliefs are used to predict tool life. While this work uses the Taylor model, by following the procedures described here, the technique can be applied to other tool life models as well.","owner":{"id":41444068,"first_name":"Tony","middle_initials":null,"last_name":"Schmitz","page_name":"TonySchmitz","domain_name":"uncc","created_at":"2016-01-13T18:28:23.678-08:00","display_name":"Tony Schmitz","url":"https://uncc.academia.edu/TonySchmitz"},"attachments":[{"id":45111949,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/45111949/thumbnails/1.jpg","file_name":"tool_life_random_walk.pdf","download_url":"https://www.academia.edu/attachments/45111949/download_file","bulk_download_file_name":"TOOL_LIFE_PREDICTION_USING_RANDOM_WALK_B.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/45111949/tool_life_random_walk-libre.pdf?1461704816=\u0026response-content-disposition=attachment%3B+filename%3DTOOL_LIFE_PREDICTION_USING_RANDOM_WALK_B.pdf\u0026Expires=1743195852\u0026Signature=XFXi2tSTx46ZlblHdpQrKkWilonNX3ZZfJPhDBNP4u9daeV4PXFT6RUseMhc3Cl5GvO4tOANHMJOWAh11ShzVtGumoyBHFFk0AsDFE2O9j3I4RUlh1xa19sPDF9q2CqfYkI0uvNRIfZBarhbwAs-GqcqNNxI0MXTlPQarnRiOR-cMAml1qWRmoAFQfyif8wBh35ytDwh76CZUjTc0R7XgHPSwWCyw1mz~Ik5K5pIFiIwaFg3cXkDnxG9AzrLRSBkGkhuaGWJy01bcESo6E6-S74QkJdDy-DK2i52tUCnzINszR2-EETtxm3L9WoEgttxoGg10llD5AM9B9tZ6-FRQw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":61603,"name":"Uncertainty","url":"https://www.academia.edu/Documents/in/Uncertainty"},{"id":78086,"name":"Random Walk","url":"https://www.academia.edu/Documents/in/Random_Walk"},{"id":96825,"name":"Manufacturing Engineering","url":"https://www.academia.edu/Documents/in/Manufacturing_Engineering"}],"urls":[{"id":6208458,"url":"http://www.tandfonline.com/doi/abs/10.1080/10910344.2013.806103?queryID=$%7BresultBean.queryID%7D"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484110-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484109"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20484109/A_METHOD_FOR_PREDICTING_CHATTER_STABILITY_FOR_SYSTEMS_WITH_SPEED_DEPENDENT_SPINDLE_DYNAMICS"><img alt="Research paper thumbnail of A METHOD FOR PREDICTING CHATTER STABILITY FOR SYSTEMS WITH SPEED-DEPENDENT SPINDLE DYNAMICS" class="work-thumbnail" src="https://attachments.academia-assets.com/44701688/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20484109/A_METHOD_FOR_PREDICTING_CHATTER_STABILITY_FOR_SYSTEMS_WITH_SPEED_DEPENDENT_SPINDLE_DYNAMICS">A METHOD FOR PREDICTING CHATTER STABILITY FOR SYSTEMS WITH SPEED-DEPENDENT SPINDLE DYNAMICS</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... Tony L. Schmitz, John C. Ziegert, Charles Stanislaus Department of Mechanical and Aerospace E...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... Tony L. Schmitz, John C. Ziegert, Charles Stanislaus Department of Mechanical and Aerospace Engineering University of Florida Gainesville, FL KEYWORDS ... [1998], Davies et al. [1998], Schmitz and Donaldson [2000], Schmitz et al. [2001]), holder characteristics (Agapiou et al. ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3027a1b28be11298d685432f0c56d00c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":44701688,"asset_id":20484109,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/44701688/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484109"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484109"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484109; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484109]").text(description); $(".js-view-count[data-work-id=20484109]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484109; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484109']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "3027a1b28be11298d685432f0c56d00c" } } $('.js-work-strip[data-work-id=20484109]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484109,"title":"A METHOD FOR PREDICTING CHATTER STABILITY FOR SYSTEMS WITH SPEED-DEPENDENT SPINDLE DYNAMICS","translated_title":"","metadata":{"abstract":"... 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484109-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484108"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/20484108/A_Study_of_Linear_Joint_and_Tool_Models_in_Spindle_Holder_Tool_Receptance_Coupling"><img alt="Research paper thumbnail of A Study of Linear Joint and Tool Models in Spindle-Holder-Tool Receptance Coupling" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">A Study of Linear Joint and Tool Models in Spindle-Holder-Tool Receptance Coupling</div><div class="wp-workCard_item"><span>Volume 6: 5th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Parts A, B, and C</span><span>, 2005</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484108"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484108"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484108; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484108-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484107"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/20484107/Improving_the_Fabrication_Process_of_Micro_Air_Vehicle_Flapping_Wings"><img alt="Research paper thumbnail of Improving the Fabrication Process of Micro-Air-Vehicle Flapping Wings" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Improving the Fabrication Process of Micro-Air-Vehicle Flapping Wings</div><div class="wp-workCard_item"><span>AIAA Journal</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT The aerodynamic performance of flapping micro air vehicles in hover conditions is depend...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT The aerodynamic performance of flapping micro air vehicles in hover conditions is dependent on many parameters, including the wing design. With the goal of optimizing the wing for hover performance, the initial focus was to reduce the uncertainty in the thrust measurements. This is because lower uncertainty in this metric enables better resolution in comparing the performance of different designs. Aerodynamic performance variability was deemed to be the fault of an imprecise manufacturing technique. Therefore, adjustments were made to the fabrication process until a permissible level of uncertainty was attained for optimization; the goal was less than 5%. This paper chronicles the progression of the wing fabrication process and details how the uncertainty was evaluated. Four fabrication methods and two different wing designs are included in this study: a carbon fiber hand layup technique, carbon fiber cured in a machined mold, and two variations of a machined plastic skeleton reinforced with a carbon fiber rod. The uncertainty in thrust production, expressed in coefficient of variation, improved from 16.8% for the hand layup method to 2.6% for the computer numerically controlled plastic skeleton adhered to the nylon membrane with transfer tape. Additionally, the coefficient of variation for wing weight also reduced (from 11.4 to 2.0%). Read More: <a href="http://arc.aiaa.org/doi/abs/10.2514/1.J053884" rel="nofollow">http://arc.aiaa.org/doi/abs/10.2514/1.J053884</a></span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484107"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484107"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484107; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484107]").text(description); $(".js-view-count[data-work-id=20484107]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484107; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484107']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=20484107]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484107,"title":"Improving the Fabrication Process of Micro-Air-Vehicle Flapping Wings","translated_title":"","metadata":{"abstract":"ABSTRACT The aerodynamic performance of flapping micro air vehicles in hover conditions is dependent on many parameters, including the wing design. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484102-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484101"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/20484101/Tool_wear_monitoring_using_na%C3%AFve_Bayes_classifiers"><img alt="Research paper thumbnail of Tool wear monitoring using naïve Bayes classifiers" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Tool wear monitoring using naïve Bayes classifiers</div><div class="wp-workCard_item"><span>The International Journal of Advanced Manufacturing Technology</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT A naïve Bayes classifier method for tool condition monitoring is described. End-milling ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT A naïve Bayes classifier method for tool condition monitoring is described. End-milling tests were performed at different spindle speeds and the cutting force was measured using a table-mounted dynamometer. The effect of tool wear on force features in the time and frequency domains was evaluated and used for training the classifier. The amount of tool wear was predicted using the naïve Bayes classifier method. Two cases are presented. First, the tool wear is divided into discrete states based on the amount of flank wear and the probability of the tool wear being in any state is updated using force data. 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Two cases are presented. First, the tool wear is divided into discrete states based on the amount of flank wear and the probability of the tool wear being in any state is updated using force data. Second, a continuous case is considered and the probability density function of the tool flank wear width is updated. 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Tool Geometry and Tool Wear Kolar, Fojtu, and Schmitz Cutting Force Coefficient Model w.r.t. Tool Geometry and Tool Wear Kolar, Fojtu, and Schmitz 710 Cutting Force Coefficient Model w.r.t. Tool Geometry and Tool Wear Kolar, Fojtu, and Schmitz 711 IODQN LV QRW LPSRUWDQW +RZHYHU WKH IODQN ZHDU DOVR LQIOXHQFHV WKH ORFDO YDOXHV RI WKH UDNH DQJOH 7KHUHIRUH WKH IODQN ZHDU VHHPV WR EH DV LPSRUWDQW DV WRRO UDNH JHRPHWU\ )LJXUH 5HVLGXDO SORWV IRU DFWLYH FXWWLQJ IRUFH ) $ )LJXUH 6HQVLWLYLW\ RI DFWLYH IRUFH ) $ WR FXWWLQJ HGJH JHRPHWU\ DQG FXWWLQJ FRQGLWLRQV Cutting Force Coefficient Model w.r.t. Tool Geometry and Tool Wear Kolar, Fojtu, and Schmitz 712 Cutting Force Coefficient Model w.r.t. Tool Geometry and Tool Wear Kolar, Fojtu, and Schmitz 717 Cutting Force Coefficient Model w.r.t. 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The temperature estimates are used to determine activation energy for thermally activated chemical wear of a PCD tool milling Monel 400 (Cu 32, Ni 65 wt%). Activation energy estimates are from 15 to 60 kJ / mole, depending on systematic uncertainties, such as diamond thermal conductivity, that still need to be addressed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="efa0ce3f19cf3aa4947266faa2b58158" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45111653,"asset_id":20484140,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45111653/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484140"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484140"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484140; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484140]").text(description); $(".js-view-count[data-work-id=20484140]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484140; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484140']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "efa0ce3f19cf3aa4947266faa2b58158" } } $('.js-work-strip[data-work-id=20484140]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484140,"title":"Estimation of Cutting Conditions in Precision Micromachining of CuNi Alloys of Varying Composition","translated_title":"","metadata":{"ai_title_tag":"Micromachining CuNi Alloys: Cutting Condition Model","grobid_abstract":"A non-dimensional model is developed that relates cutting edge temperatures to process inputs (speed and feed) and outputs (cutting and thrust forces) as well as tool and work thermal properties in micromachining with diamond tools. The temperature estimates are used to determine activation energy for thermally activated chemical wear of a PCD tool milling Monel 400 (Cu 32, Ni 65 wt%). 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The temperature estimates are used to determine activation energy for thermally activated chemical wear of a PCD tool milling Monel 400 (Cu 32, Ni 65 wt%). 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In this approach, the optical interference signal is recorded during constant velocity target motion using a spectrum analyzer and the magnitudes of the individual periodic error contributors are used to calculate error magnitudes. This study builds on prior work by treating the general case where both first and second order error components exist and arbitrary initial phase values are considered. Significant experimental results are presented which verify the new approach.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8e71a55146d2709b67eeea423b10806d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41397779,"asset_id":20484138,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41397779/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484138"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484138"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484138; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484138]").text(description); $(".js-view-count[data-work-id=20484138]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484138; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484138']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8e71a55146d2709b67eeea423b10806d" } } $('.js-work-strip[data-work-id=20484138]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484138,"title":"Periodic error calculation from spectrum analyzer data","translated_title":"","metadata":{"ai_title_tag":"Monte Carlo Analysis of Periodic Errors","grobid_abstract":"This paper describes the Monte Carlo evaluation of a single equation that can be used to determine periodic error magnitudes from spectrum analyzer data. 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Significant experimental results are presented which verify the new approach.","owner":{"id":41444068,"first_name":"Tony","middle_initials":null,"last_name":"Schmitz","page_name":"TonySchmitz","domain_name":"uncc","created_at":"2016-01-13T18:28:23.678-08:00","display_name":"Tony Schmitz","url":"https://uncc.academia.edu/TonySchmitz"},"attachments":[{"id":41397779,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41397779/thumbnails/1.jpg","file_name":"Periodic_error_calculation_from_spectrum20160122-17374-cljksa.pdf","download_url":"https://www.academia.edu/attachments/41397779/download_file","bulk_download_file_name":"Periodic_error_calculation_from_spectrum.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41397779/Periodic_error_calculation_from_spectrum20160122-17374-cljksa-libre.pdf?1453452252=\u0026response-content-disposition=attachment%3B+filename%3DPeriodic_error_calculation_from_spectrum.pdf\u0026Expires=1743195852\u0026Signature=AIND15CFT6NyNw7LUBZ59mqpsHcfjSU0xz0ooEwQ25LbC8TpGZ5J9eB1GdRDeAO316~NA3zF4A28LZ7U8WRELkjMLXXBgfnsWXRt3ZRgyZwkZXO9QgtbiHnHuRrxaQvcKeAubDQXzJ7ybdGN3UNm6D4pmSafT982ZlD5sjMJiXrIcYCkg6vTxaUVPuAvvgDnOnXIE2140gKApoZwtab7QU5k5htj4f5mvo~O16tzF-PE928H~PHTeJ2t-c2A3YYsSgLHSriDnZ-7gDtUJBYjM2~ANzlZL-19-r78w375U9uB1MRLE0ggqFcWqODbeyDhHsSRMu3vX~rTgLZQPELz7g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":923,"name":"Technology","url":"https://www.academia.edu/Documents/in/Technology"},{"id":118582,"name":"Physical sciences","url":"https://www.academia.edu/Documents/in/Physical_sciences"},{"id":1286358,"name":"Precision Engineering","url":"https://www.academia.edu/Documents/in/Precision_Engineering"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484138-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484135"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20484135/Sensor_design_and_evaluation_for_on_machine_probing_of_extruded_tool_joints"><img alt="Research paper thumbnail of Sensor design and evaluation for on-machine probing of extruded tool joints" class="work-thumbnail" src="https://attachments.academia-assets.com/45111704/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20484135/Sensor_design_and_evaluation_for_on_machine_probing_of_extruded_tool_joints">Sensor design and evaluation for on-machine probing of extruded tool joints</a></div><div class="wp-workCard_item"><span>Precision Engineering</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT This paper describes the design and evaluation of two contact probes used to measure the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT This paper describes the design and evaluation of two contact probes used to measure the length and bore concentricity of cylindrical, extruded tool joints while clamped in a production lathe spindle. The probes consisted of an LVDT, a spring-preloaded shaft supported by linear bearings used to isolate the LVDT from side loads, and a hardened steel sphere to contact the rough surface. For bore concentricity measurements, a parallelogram leaf-type flexure and 45° surface was used to transfer radial deviations to the spindle/part/LVDT axis. The LVDT output was used in conjunction with the lathe turret position to determine the extruded part dimensions prior to machining. Experimental results are provided for measurements of multiple parts; variations in length, internal diameter, and bore concentricity are compared to the nominal dimensions. Additionally, a calibration artifact is described which enabled evaluation of the measurement accuracies for the two probes. Given the pre-machining part dimensions, it is shown how this information can be used to select from a pre-defined matrix of part programs to reduce cycle time and machining cost.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9d119679a5546ce784b28e3c3736eba9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45111704,"asset_id":20484135,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45111704/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484135"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484135"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484135; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484135]").text(description); $(".js-view-count[data-work-id=20484135]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484135; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484135']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9d119679a5546ce784b28e3c3736eba9" } } $('.js-work-strip[data-work-id=20484135]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484135,"title":"Sensor design and evaluation for on-machine probing of extruded tool joints","translated_title":"","metadata":{"abstract":"ABSTRACT This paper describes the design and evaluation of two contact probes used to measure the length and bore concentricity of cylindrical, extruded tool joints while clamped in a production lathe spindle. The probes consisted of an LVDT, a spring-preloaded shaft supported by linear bearings used to isolate the LVDT from side loads, and a hardened steel sphere to contact the rough surface. For bore concentricity measurements, a parallelogram leaf-type flexure and 45° surface was used to transfer radial deviations to the spindle/part/LVDT axis. The LVDT output was used in conjunction with the lathe turret position to determine the extruded part dimensions prior to machining. Experimental results are provided for measurements of multiple parts; variations in length, internal diameter, and bore concentricity are compared to the nominal dimensions. Additionally, a calibration artifact is described which enabled evaluation of the measurement accuracies for the two probes. Given the pre-machining part dimensions, it is shown how this information can be used to select from a pre-defined matrix of part programs to reduce cycle time and machining cost.","publication_date":{"day":null,"month":null,"year":2011,"errors":{}},"publication_name":"Precision Engineering"},"translated_abstract":"ABSTRACT This paper describes the design and evaluation of two contact probes used to measure the length and bore concentricity of cylindrical, extruded tool joints while clamped in a production lathe spindle. The probes consisted of an LVDT, a spring-preloaded shaft supported by linear bearings used to isolate the LVDT from side loads, and a hardened steel sphere to contact the rough surface. For bore concentricity measurements, a parallelogram leaf-type flexure and 45° surface was used to transfer radial deviations to the spindle/part/LVDT axis. The LVDT output was used in conjunction with the lathe turret position to determine the extruded part dimensions prior to machining. Experimental results are provided for measurements of multiple parts; variations in length, internal diameter, and bore concentricity are compared to the nominal dimensions. Additionally, a calibration artifact is described which enabled evaluation of the measurement accuracies for the two probes. Given the pre-machining part dimensions, it is shown how this information can be used to select from a pre-defined matrix of part programs to reduce cycle time and machining cost.","internal_url":"https://www.academia.edu/20484135/Sensor_design_and_evaluation_for_on_machine_probing_of_extruded_tool_joints","translated_internal_url":"","created_at":"2016-01-22T00:40:49.218-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41444068,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":45111704,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/45111704/thumbnails/1.jpg","file_name":"on_machine_probing.pdf","download_url":"https://www.academia.edu/attachments/45111704/download_file","bulk_download_file_name":"Sensor_design_and_evaluation_for_on_mach.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/45111704/on_machine_probing-libre.pdf?1461703982=\u0026response-content-disposition=attachment%3B+filename%3DSensor_design_and_evaluation_for_on_mach.pdf\u0026Expires=1743195852\u0026Signature=HGZkCexpVAWRDybXEKAUlHen1piN-yhre44voYXlx6LsDWYELm-aUQzcwwDRGK9E-qOr6HczAOVGk0jX485B3cIAJlhWHL-Ud~Dig8YTHp229m1oVVQ10kLXor9dqIpXkqJ49sJL8LocJn-czQw~v0Uh0cDogM3WVH9WDeU6GmvmvShAdljV2qttkuVjhuBVpOMVvsE8dDJp~wk2Z6AST6fIVeQPfUIAwxYW1n~aakO~cf8nIelwPz6~4patd0QpWSd~SatM3nm3fQ949ZXFYOGDcWWeYrgdq2~c-1tneE9SoA6ycgCNVFQIhAOh69NtyfXenGireSfiLhF5Itsthg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Sensor_design_and_evaluation_for_on_machine_probing_of_extruded_tool_joints","translated_slug":"","page_count":11,"language":"en","content_type":"Work","summary":"ABSTRACT This paper describes the design and evaluation of two contact probes used to measure the length and bore concentricity of cylindrical, extruded tool joints while clamped in a production lathe spindle. The probes consisted of an LVDT, a spring-preloaded shaft supported by linear bearings used to isolate the LVDT from side loads, and a hardened steel sphere to contact the rough surface. For bore concentricity measurements, a parallelogram leaf-type flexure and 45° surface was used to transfer radial deviations to the spindle/part/LVDT axis. The LVDT output was used in conjunction with the lathe turret position to determine the extruded part dimensions prior to machining. Experimental results are provided for measurements of multiple parts; variations in length, internal diameter, and bore concentricity are compared to the nominal dimensions. Additionally, a calibration artifact is described which enabled evaluation of the measurement accuracies for the two probes. Given the pre-machining part dimensions, it is shown how this information can be used to select from a pre-defined matrix of part programs to reduce cycle time and machining cost.","owner":{"id":41444068,"first_name":"Tony","middle_initials":null,"last_name":"Schmitz","page_name":"TonySchmitz","domain_name":"uncc","created_at":"2016-01-13T18:28:23.678-08:00","display_name":"Tony Schmitz","url":"https://uncc.academia.edu/TonySchmitz"},"attachments":[{"id":45111704,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/45111704/thumbnails/1.jpg","file_name":"on_machine_probing.pdf","download_url":"https://www.academia.edu/attachments/45111704/download_file","bulk_download_file_name":"Sensor_design_and_evaluation_for_on_mach.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/45111704/on_machine_probing-libre.pdf?1461703982=\u0026response-content-disposition=attachment%3B+filename%3DSensor_design_and_evaluation_for_on_mach.pdf\u0026Expires=1743195852\u0026Signature=HGZkCexpVAWRDybXEKAUlHen1piN-yhre44voYXlx6LsDWYELm-aUQzcwwDRGK9E-qOr6HczAOVGk0jX485B3cIAJlhWHL-Ud~Dig8YTHp229m1oVVQ10kLXor9dqIpXkqJ49sJL8LocJn-czQw~v0Uh0cDogM3WVH9WDeU6GmvmvShAdljV2qttkuVjhuBVpOMVvsE8dDJp~wk2Z6AST6fIVeQPfUIAwxYW1n~aakO~cf8nIelwPz6~4patd0QpWSd~SatM3nm3fQ949ZXFYOGDcWWeYrgdq2~c-1tneE9SoA6ycgCNVFQIhAOh69NtyfXenGireSfiLhF5Itsthg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":923,"name":"Technology","url":"https://www.academia.edu/Documents/in/Technology"},{"id":118582,"name":"Physical sciences","url":"https://www.academia.edu/Documents/in/Physical_sciences"},{"id":1286358,"name":"Precision Engineering","url":"https://www.academia.edu/Documents/in/Precision_Engineering"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484135-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484132"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20484132/Chatter_recognition_by_a_statistical_evaluation_of_the_synchronously_sampled_audio_signal"><img alt="Research paper thumbnail of Chatter recognition by a statistical evaluation of the synchronously sampled audio signal" class="work-thumbnail" src="https://attachments.academia-assets.com/45111870/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20484132/Chatter_recognition_by_a_statistical_evaluation_of_the_synchronously_sampled_audio_signal">Chatter recognition by a statistical evaluation of the synchronously sampled audio signal</a></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-20484132-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-20484132-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666572/figure-1-fifty-per-cent-radial-immersion-milling-schematic"><img alt="Fig. 1. Fifty per cent radial immersion milling schematic; cutter rotation is clockwise. T.L. Schmitz | Journal of Sound and Vibration 262 (2003) 721-730 " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666590/figure-2-example-stability-lobe-diagram-showing-separation"><img alt="Fig. 2. Example stability lobe diagram showing separation between stable and unstable cutting conditions as a function of allowable chip width and spindle speed. " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666608/figure-3-fifty-per-cent-radial-immersion-scutting-example"><img alt="Fig. 3. Fifty per cent radial immersion stable cutting example. The notion of a statistical evaluation of the once-per-revolution milling audio signal to detec 1atter is based on Poincaré mapping techniques, where a local description of transient behavior constructed from the Poincaré map and the system stability may be established [17]. For milling le stability can be evaluated by plotting the x direction versus y direction tool motions and lentifying the once-per-revolution sampled data points [18]. For stable cutting, the mchronously sampled points approach a fixed point for the Poincaré map after some initia ansients and, thus, provide a tight distribution. Physically, this means that, although the tool is brating in the two orthogonal directions, the motions are synchronous with spindle rotation and le tool is returning to approximately the same position in each revolution under steady state ynditions. In contrast, tool motions during regenerative chatter are not synchronous with spindle tation; instead, they occur near the natural frequency corresponding to the most flexible system ode due to the nature of self-excited vibrations. For these unstable cuts, the tool does not return ) the same position each revolution. Rather, the once-per-revolution sampled distribution can nd toward an elliptical shape due to the quasi-periodic nature of chatter. The two cases are 10wn in Figs. 3 and 4. In both instances, simulated x and y direction tool motions, as well as the oe " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666627/figure-5-histogram-of-once-per-revolution-points-for-radial"><img alt="Fig. 5. Histogram of once-per-revolution points for stable 50% radial immersion cut. Fig. 4. Fifty per cent radial immersion unstable cutting example. " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666648/figure-5-schmitz-journal-of-sound-and-vibration"><img alt="T.L. Schmitz | Journal of Sound and Vibration 262 (2003) 721-730 " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666671/figure-6-histogram-of-once-per-revolution-points-for-uns"><img alt="Fig. 6. Histogram of once-per-revolution points for unstable 50% radial immersion cut. T.L. Schmitz | Journal of Sound and Vibration 262 (2003) 721-730 " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_006.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666688/figure-7-set-up-for-cutting-tests-showing-microphone-and"><img alt="Fig. 7. Set-up for cutting tests showing microphone and infrared emitter/detector pair. T.L. Schmitz | Journal of Sound and Vibration 262 (2003) 721-730 " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_007.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666706/figure-8-variance-values-in-mv-for-cutting-tests-using"><img alt="Fig. 8. Variance values in mV for cutting tests using emitter/detector pair once-per-revolution trigger. T.L. Schmitz | Journal of Sound and Vibration 262 (2003) 721-730 " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_008.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666723/figure-9-variance-values-in-mv-for-cutting-tests-using"><img alt="Fig. 9. Variance values in mV~ for cutting tests using synthetic actual spindle speed once-per-revolution trigger. T.L. Schmitz | Journal of Sound and Vibration 262 (2003) 721-730 " class="figure-slide-image" src="https://figures.academia-assets.com/45111870/figure_009.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/10666741/figure-10-variance-values-in-mv-for-cutting-tests-using"><img alt="Fig. 10. Variance values in mV? for cutting tests using synthetic nominal spindle speed once-per-revolution trigger. T.L. Schmitz | Journal of Sound and Vibration 262 (2003) 721-730 variance values for a number of stable and unstable cuts was completed for various strategies in obtaining the once-per-revolution trigger, which included: (1) obtaining the once-per-revolution signal using a locally derived trigger; (2) generating a synthetic once-per-revolution sampling trigger using the actual spindle speed; and (3) producing the once-per-revolution signal from the nominal (commanded) spindle speed. In all cases, the calculated variance values detected the transition from stable to unstable cutting. 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The method leverages the variance in cutting data, reducing the need for extensive signal processing typically required in frequency domain analysis. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484112-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484111"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20484111/In_situ_monitoring_and_prediction_of_progressive_joint_wear_using_Bayesian_statistics"><img alt="Research paper thumbnail of In situ monitoring and prediction of progressive joint wear using Bayesian statistics" class="work-thumbnail" src="https://attachments.academia-assets.com/41397737/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20484111/In_situ_monitoring_and_prediction_of_progressive_joint_wear_using_Bayesian_statistics">In situ monitoring and prediction of progressive joint wear using Bayesian statistics</a></div><div class="wp-workCard_item"><span>Wear</span><span>, May 1, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, a statistical methodology of estimating wear coefficient and predicting wear volum...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, a statistical methodology of estimating wear coefficient and predicting wear volume in a revolute joint using in situ measurement data is presented. An instrumented slider-crank mechanism that can measure the joint force and the relative motion between the pin and bushing is built. The former is measured using a load cell built onto a necked portion of the hollow steel pin, while the latter is measured using a capacitance probe. In order to isolate the effect of friction in other joints, a porous carbon air bearing for the revolute joint between the follower link and the slide stage, as well as a prismatic joint for the linear slide, are used. Based on the relative motion between the centers of the pin and bushing, the wear volumes are estimated at six different operating cycles. The Bayesian inference technique is used to update the distribution of wear coefficients, which incorporates in situ measurement data to obtain the posterior distribution. The Markov Chain Monte Carlo technique is employed to generate samples from the given distribution. The results show that it is possible to narrow the distribution of wear coefficients and to predict the future wear volume with reasonable confidence. The effect of the prior distribution on the wear coefficient is discussed by comparing with the non-informative case.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="28675567ebc59647a37f24370e1347f5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41397737,"asset_id":20484111,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41397737/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484111"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484111"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484111; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484111]").text(description); $(".js-view-count[data-work-id=20484111]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484111; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484111']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "28675567ebc59647a37f24370e1347f5" } } $('.js-work-strip[data-work-id=20484111]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484111,"title":"In situ monitoring and prediction of progressive joint wear using Bayesian statistics","translated_title":"","metadata":{"ai_title_tag":"Bayesian In Situ Joint Wear Monitoring and Prediction","grobid_abstract":"In this paper, a statistical methodology of estimating wear coefficient and predicting wear volume in a revolute joint using in situ measurement data is presented. An instrumented slider-crank mechanism that can measure the joint force and the relative motion between the pin and bushing is built. The former is measured using a load cell built onto a necked portion of the hollow steel pin, while the latter is measured using a capacitance probe. In order to isolate the effect of friction in other joints, a porous carbon air bearing for the revolute joint between the follower link and the slide stage, as well as a prismatic joint for the linear slide, are used. Based on the relative motion between the centers of the pin and bushing, the wear volumes are estimated at six different operating cycles. The Bayesian inference technique is used to update the distribution of wear coefficients, which incorporates in situ measurement data to obtain the posterior distribution. The Markov Chain Monte Carlo technique is employed to generate samples from the given distribution. The results show that it is possible to narrow the distribution of wear coefficients and to predict the future wear volume with reasonable confidence. 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An instrumented slider-crank mechanism that can measure the joint force and the relative motion between the pin and bushing is built. The former is measured using a load cell built onto a necked portion of the hollow steel pin, while the latter is measured using a capacitance probe. In order to isolate the effect of friction in other joints, a porous carbon air bearing for the revolute joint between the follower link and the slide stage, as well as a prismatic joint for the linear slide, are used. Based on the relative motion between the centers of the pin and bushing, the wear volumes are estimated at six different operating cycles. The Bayesian inference technique is used to update the distribution of wear coefficients, which incorporates in situ measurement data to obtain the posterior distribution. The Markov Chain Monte Carlo technique is employed to generate samples from the given distribution. The results show that it is possible to narrow the distribution of wear coefficients and to predict the future wear volume with reasonable confidence. The effect of the prior distribution on the wear coefficient is discussed by comparing with the non-informative case.","owner":{"id":41444068,"first_name":"Tony","middle_initials":null,"last_name":"Schmitz","page_name":"TonySchmitz","domain_name":"uncc","created_at":"2016-01-13T18:28:23.678-08:00","display_name":"Tony Schmitz","url":"https://uncc.academia.edu/TonySchmitz"},"attachments":[{"id":41397737,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41397737/thumbnails/1.jpg","file_name":"In-situ_monitoring_and_prediction_of_pro20160122-7958-uejvlz.pdf","download_url":"https://www.academia.edu/attachments/41397737/download_file","bulk_download_file_name":"In_situ_monitoring_and_prediction_of_pro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41397737/In-situ_monitoring_and_prediction_of_pro20160122-7958-uejvlz-libre.pdf?1453452219=\u0026response-content-disposition=attachment%3B+filename%3DIn_situ_monitoring_and_prediction_of_pro.pdf\u0026Expires=1743195852\u0026Signature=V6IYr4ymG1WOGHTKOPMfl---6RQ9udPL8MNoSmuMzIqC7b5-~ph33bXAmqz~waRHZJ5FtNguaHB2aAbzuh~HRK-oyO-CYSjRBm-6SvmIf2ru-TAfSAIEAe18BLDUdptmCVemOMNnue8voSZN248J46idnw6Ty6YSrZyMhLx36JoWBb-xXfhl4e84SduHkz7WZfEB-acpJSneIQlexXLY5wsyEA~aCg47vo~DsBUqjgFsSer5Jx5c5MLdd9e6SIYPcFrwHwqmVHqwg-9JERMh4BTF3b-XDEEKv5FfEUC3-LO5whnfgTMN9YdLX918lPdY89L7TVnuS~XvMytbI-DhJA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":56,"name":"Materials Engineering","url":"https://www.academia.edu/Documents/in/Materials_Engineering"},{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering"},{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis"},{"id":6177,"name":"Modeling","url":"https://www.academia.edu/Documents/in/Modeling"},{"id":23008,"name":"Wear","url":"https://www.academia.edu/Documents/in/Wear"},{"id":23032,"name":"Tribology","url":"https://www.academia.edu/Documents/in/Tribology"},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference"},{"id":61603,"name":"Uncertainty","url":"https://www.academia.edu/Documents/in/Uncertainty"},{"id":85262,"name":"Markov Chain Monte Carlo","url":"https://www.academia.edu/Documents/in/Markov_Chain_Monte_Carlo"},{"id":100094,"name":"Bayesian statistics","url":"https://www.academia.edu/Documents/in/Bayesian_statistics"},{"id":389570,"name":"Capacitance","url":"https://www.academia.edu/Documents/in/Capacitance"},{"id":1333436,"name":"Monte Carlo Method","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Method"},{"id":1554438,"name":"Load Cell","url":"https://www.academia.edu/Documents/in/Load_Cell"}],"urls":[{"id":6208459,"url":"http://cat.inist.fr/?aModele=afficheN\u0026cpsidt=24155097"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484111-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484110"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20484110/TOOL_LIFE_PREDICTION_USING_RANDOM_WALK_BAYESIAN_UPDATING"><img alt="Research paper thumbnail of TOOL LIFE PREDICTION USING RANDOM WALK BAYESIAN UPDATING" class="work-thumbnail" src="https://attachments.academia-assets.com/45111949/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20484110/TOOL_LIFE_PREDICTION_USING_RANDOM_WALK_BAYESIAN_UPDATING">TOOL LIFE PREDICTION USING RANDOM WALK BAYESIAN UPDATING</a></div><div class="wp-workCard_item"><span>Http Dx Doi Org 10 1080 10910344 2013 806103</span><span>, Jul 3, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT According to the Taylor tool life equation, tool life reduces with increasing cutting sp...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT According to the Taylor tool life equation, tool life reduces with increasing cutting speed. The influence of additional factors can also be incorporated. However, tool wear is generally considered a stochastic process with uncertainty in the model constants. In this work, Bayesian inference is applied to predict tool life for milling/turning operations using the random walk/surface methods. For milling, Bayesian inference using a random walk approach is applied to the well-known Taylor tool life model. Tool wear tests are performed using an uncoated carbide tool and AISI 1018 steel work material. Test results are used to update the probability distribution of tool life. The updated beliefs are then applied to predict tool life using a probability distribution. For turning, both cutting speed and feed are considered. Bayesian updating is performed using the random surface technique. Turning tests are completed using a coated carbide tool and forged AISI 4137 chrome alloy steel. The test results are applied to update the probability distribution of tool life and the updated beliefs are used to predict tool life. While this work uses the Taylor model, by following the procedures described here, the technique can be applied to other tool life models as well.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ac57b23cefe0788caf6a2f8c9054de38" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45111949,"asset_id":20484110,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45111949/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484110"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484110"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484110; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484110]").text(description); $(".js-view-count[data-work-id=20484110]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484110; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484110']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ac57b23cefe0788caf6a2f8c9054de38" } } $('.js-work-strip[data-work-id=20484110]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484110,"title":"TOOL LIFE PREDICTION USING RANDOM WALK BAYESIAN UPDATING","translated_title":"","metadata":{"abstract":"ABSTRACT According to the Taylor tool life equation, tool life reduces with increasing cutting speed. The influence of additional factors can also be incorporated. However, tool wear is generally considered a stochastic process with uncertainty in the model constants. In this work, Bayesian inference is applied to predict tool life for milling/turning operations using the random walk/surface methods. For milling, Bayesian inference using a random walk approach is applied to the well-known Taylor tool life model. Tool wear tests are performed using an uncoated carbide tool and AISI 1018 steel work material. Test results are used to update the probability distribution of tool life. The updated beliefs are then applied to predict tool life using a probability distribution. For turning, both cutting speed and feed are considered. Bayesian updating is performed using the random surface technique. Turning tests are completed using a coated carbide tool and forged AISI 4137 chrome alloy steel. The test results are applied to update the probability distribution of tool life and the updated beliefs are used to predict tool life. While this work uses the Taylor model, by following the procedures described here, the technique can be applied to other tool life models as well.","ai_title_tag":"Bayesian Tool Life Prediction via Random Walk","publication_date":{"day":3,"month":7,"year":2013,"errors":{}},"publication_name":"Http Dx Doi Org 10 1080 10910344 2013 806103"},"translated_abstract":"ABSTRACT According to the Taylor tool life equation, tool life reduces with increasing cutting speed. The influence of additional factors can also be incorporated. However, tool wear is generally considered a stochastic process with uncertainty in the model constants. In this work, Bayesian inference is applied to predict tool life for milling/turning operations using the random walk/surface methods. For milling, Bayesian inference using a random walk approach is applied to the well-known Taylor tool life model. Tool wear tests are performed using an uncoated carbide tool and AISI 1018 steel work material. Test results are used to update the probability distribution of tool life. The updated beliefs are then applied to predict tool life using a probability distribution. For turning, both cutting speed and feed are considered. Bayesian updating is performed using the random surface technique. Turning tests are completed using a coated carbide tool and forged AISI 4137 chrome alloy steel. The test results are applied to update the probability distribution of tool life and the updated beliefs are used to predict tool life. While this work uses the Taylor model, by following the procedures described here, the technique can be applied to other tool life models as well.","internal_url":"https://www.academia.edu/20484110/TOOL_LIFE_PREDICTION_USING_RANDOM_WALK_BAYESIAN_UPDATING","translated_internal_url":"","created_at":"2016-01-22T00:40:18.429-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41444068,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":45111949,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/45111949/thumbnails/1.jpg","file_name":"tool_life_random_walk.pdf","download_url":"https://www.academia.edu/attachments/45111949/download_file","bulk_download_file_name":"TOOL_LIFE_PREDICTION_USING_RANDOM_WALK_B.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/45111949/tool_life_random_walk-libre.pdf?1461704816=\u0026response-content-disposition=attachment%3B+filename%3DTOOL_LIFE_PREDICTION_USING_RANDOM_WALK_B.pdf\u0026Expires=1743195852\u0026Signature=XFXi2tSTx46ZlblHdpQrKkWilonNX3ZZfJPhDBNP4u9daeV4PXFT6RUseMhc3Cl5GvO4tOANHMJOWAh11ShzVtGumoyBHFFk0AsDFE2O9j3I4RUlh1xa19sPDF9q2CqfYkI0uvNRIfZBarhbwAs-GqcqNNxI0MXTlPQarnRiOR-cMAml1qWRmoAFQfyif8wBh35ytDwh76CZUjTc0R7XgHPSwWCyw1mz~Ik5K5pIFiIwaFg3cXkDnxG9AzrLRSBkGkhuaGWJy01bcESo6E6-S74QkJdDy-DK2i52tUCnzINszR2-EETtxm3L9WoEgttxoGg10llD5AM9B9tZ6-FRQw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"TOOL_LIFE_PREDICTION_USING_RANDOM_WALK_BAYESIAN_UPDATING","translated_slug":"","page_count":34,"language":"en","content_type":"Work","summary":"ABSTRACT According to the Taylor tool life equation, tool life reduces with increasing cutting speed. The influence of additional factors can also be incorporated. However, tool wear is generally considered a stochastic process with uncertainty in the model constants. In this work, Bayesian inference is applied to predict tool life for milling/turning operations using the random walk/surface methods. For milling, Bayesian inference using a random walk approach is applied to the well-known Taylor tool life model. Tool wear tests are performed using an uncoated carbide tool and AISI 1018 steel work material. Test results are used to update the probability distribution of tool life. The updated beliefs are then applied to predict tool life using a probability distribution. For turning, both cutting speed and feed are considered. Bayesian updating is performed using the random surface technique. Turning tests are completed using a coated carbide tool and forged AISI 4137 chrome alloy steel. The test results are applied to update the probability distribution of tool life and the updated beliefs are used to predict tool life. While this work uses the Taylor model, by following the procedures described here, the technique can be applied to other tool life models as well.","owner":{"id":41444068,"first_name":"Tony","middle_initials":null,"last_name":"Schmitz","page_name":"TonySchmitz","domain_name":"uncc","created_at":"2016-01-13T18:28:23.678-08:00","display_name":"Tony Schmitz","url":"https://uncc.academia.edu/TonySchmitz"},"attachments":[{"id":45111949,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/45111949/thumbnails/1.jpg","file_name":"tool_life_random_walk.pdf","download_url":"https://www.academia.edu/attachments/45111949/download_file","bulk_download_file_name":"TOOL_LIFE_PREDICTION_USING_RANDOM_WALK_B.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/45111949/tool_life_random_walk-libre.pdf?1461704816=\u0026response-content-disposition=attachment%3B+filename%3DTOOL_LIFE_PREDICTION_USING_RANDOM_WALK_B.pdf\u0026Expires=1743195852\u0026Signature=XFXi2tSTx46ZlblHdpQrKkWilonNX3ZZfJPhDBNP4u9daeV4PXFT6RUseMhc3Cl5GvO4tOANHMJOWAh11ShzVtGumoyBHFFk0AsDFE2O9j3I4RUlh1xa19sPDF9q2CqfYkI0uvNRIfZBarhbwAs-GqcqNNxI0MXTlPQarnRiOR-cMAml1qWRmoAFQfyif8wBh35ytDwh76CZUjTc0R7XgHPSwWCyw1mz~Ik5K5pIFiIwaFg3cXkDnxG9AzrLRSBkGkhuaGWJy01bcESo6E6-S74QkJdDy-DK2i52tUCnzINszR2-EETtxm3L9WoEgttxoGg10llD5AM9B9tZ6-FRQw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":61603,"name":"Uncertainty","url":"https://www.academia.edu/Documents/in/Uncertainty"},{"id":78086,"name":"Random Walk","url":"https://www.academia.edu/Documents/in/Random_Walk"},{"id":96825,"name":"Manufacturing Engineering","url":"https://www.academia.edu/Documents/in/Manufacturing_Engineering"}],"urls":[{"id":6208458,"url":"http://www.tandfonline.com/doi/abs/10.1080/10910344.2013.806103?queryID=$%7BresultBean.queryID%7D"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484110-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484109"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20484109/A_METHOD_FOR_PREDICTING_CHATTER_STABILITY_FOR_SYSTEMS_WITH_SPEED_DEPENDENT_SPINDLE_DYNAMICS"><img alt="Research paper thumbnail of A METHOD FOR PREDICTING CHATTER STABILITY FOR SYSTEMS WITH SPEED-DEPENDENT SPINDLE DYNAMICS" class="work-thumbnail" src="https://attachments.academia-assets.com/44701688/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20484109/A_METHOD_FOR_PREDICTING_CHATTER_STABILITY_FOR_SYSTEMS_WITH_SPEED_DEPENDENT_SPINDLE_DYNAMICS">A METHOD FOR PREDICTING CHATTER STABILITY FOR SYSTEMS WITH SPEED-DEPENDENT SPINDLE DYNAMICS</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... Tony L. Schmitz, John C. Ziegert, Charles Stanislaus Department of Mechanical and Aerospace E...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... Tony L. Schmitz, John C. Ziegert, Charles Stanislaus Department of Mechanical and Aerospace Engineering University of Florida Gainesville, FL KEYWORDS ... [1998], Davies et al. [1998], Schmitz and Donaldson [2000], Schmitz et al. [2001]), holder characteristics (Agapiou et al. ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3027a1b28be11298d685432f0c56d00c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":44701688,"asset_id":20484109,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/44701688/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484109"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484109"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484109; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484109]").text(description); $(".js-view-count[data-work-id=20484109]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484109; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484109']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "3027a1b28be11298d685432f0c56d00c" } } $('.js-work-strip[data-work-id=20484109]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484109,"title":"A METHOD FOR PREDICTING CHATTER STABILITY FOR SYSTEMS WITH SPEED-DEPENDENT SPINDLE DYNAMICS","translated_title":"","metadata":{"abstract":"... 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484109-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484108"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/20484108/A_Study_of_Linear_Joint_and_Tool_Models_in_Spindle_Holder_Tool_Receptance_Coupling"><img alt="Research paper thumbnail of A Study of Linear Joint and Tool Models in Spindle-Holder-Tool Receptance Coupling" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">A Study of Linear Joint and Tool Models in Spindle-Holder-Tool Receptance Coupling</div><div class="wp-workCard_item"><span>Volume 6: 5th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Parts A, B, and C</span><span>, 2005</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484108"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484108"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484108; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484108-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484107"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/20484107/Improving_the_Fabrication_Process_of_Micro_Air_Vehicle_Flapping_Wings"><img alt="Research paper thumbnail of Improving the Fabrication Process of Micro-Air-Vehicle Flapping Wings" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Improving the Fabrication Process of Micro-Air-Vehicle Flapping Wings</div><div class="wp-workCard_item"><span>AIAA Journal</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT The aerodynamic performance of flapping micro air vehicles in hover conditions is depend...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT The aerodynamic performance of flapping micro air vehicles in hover conditions is dependent on many parameters, including the wing design. With the goal of optimizing the wing for hover performance, the initial focus was to reduce the uncertainty in the thrust measurements. This is because lower uncertainty in this metric enables better resolution in comparing the performance of different designs. Aerodynamic performance variability was deemed to be the fault of an imprecise manufacturing technique. Therefore, adjustments were made to the fabrication process until a permissible level of uncertainty was attained for optimization; the goal was less than 5%. This paper chronicles the progression of the wing fabrication process and details how the uncertainty was evaluated. Four fabrication methods and two different wing designs are included in this study: a carbon fiber hand layup technique, carbon fiber cured in a machined mold, and two variations of a machined plastic skeleton reinforced with a carbon fiber rod. The uncertainty in thrust production, expressed in coefficient of variation, improved from 16.8% for the hand layup method to 2.6% for the computer numerically controlled plastic skeleton adhered to the nylon membrane with transfer tape. Additionally, the coefficient of variation for wing weight also reduced (from 11.4 to 2.0%). Read More: <a href="http://arc.aiaa.org/doi/abs/10.2514/1.J053884" rel="nofollow">http://arc.aiaa.org/doi/abs/10.2514/1.J053884</a></span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20484107"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20484107"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20484107; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20484107]").text(description); $(".js-view-count[data-work-id=20484107]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20484107; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20484107']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=20484107]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20484107,"title":"Improving the Fabrication Process of Micro-Air-Vehicle Flapping Wings","translated_title":"","metadata":{"abstract":"ABSTRACT The aerodynamic performance of flapping micro air vehicles in hover conditions is dependent on many parameters, including the wing design. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-20484102-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="20484101"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/20484101/Tool_wear_monitoring_using_na%C3%AFve_Bayes_classifiers"><img alt="Research paper thumbnail of Tool wear monitoring using naïve Bayes classifiers" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Tool wear monitoring using naïve Bayes classifiers</div><div class="wp-workCard_item"><span>The International Journal of Advanced Manufacturing Technology</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT A naïve Bayes classifier method for tool condition monitoring is described. End-milling ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT A naïve Bayes classifier method for tool condition monitoring is described. End-milling tests were performed at different spindle speeds and the cutting force was measured using a table-mounted dynamometer. The effect of tool wear on force features in the time and frequency domains was evaluated and used for training the classifier. The amount of tool wear was predicted using the naïve Bayes classifier method. Two cases are presented. First, the tool wear is divided into discrete states based on the amount of flank wear and the probability of the tool wear being in any state is updated using force data. 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Two cases are presented. First, the tool wear is divided into discrete states based on the amount of flank wear and the probability of the tool wear being in any state is updated using force data. Second, a continuous case is considered and the probability density function of the tool flank wear width is updated. 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