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The identification tool supports the AI-based optimisation of milling machine process parameters when using unknown material compositions. The process parameters are determined by a specific test pattern designed to be automatically analysed in real-time by a pre- trained perception-based deep learning algorithm. The tool provides the advantage of obtaining real-time quality information due to AI- based quality assessment and the automated identification of material- dependent milling process parameter sets, even for unknown processing material. 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Feldmann</a> </div> <div id="content"> <div id="topBar"> </div> <div id="articleTitle"><h3>Fast Automatized Parameter Adaption Process of CNC Milling Machines Under the Use of Perception Based Artificial Intelligence</h3></div> <div id="authorString"><em>S. Feldmann, M. Schmiedt, J. Jung, J. M. Schlosser, T. Stempfle, C. Rathmann, W. Rimkus</em></div> <br /> <div id="articleAbstract"> <h4>Abstract</h4> <br /> <div style="text-align: justify;">This paper concerns unpublished results obtained from the SIMKI (2020) R&D project at the Department of Mechanical Engineering at Aalen University of Applied Science, Germany. The following text generally discusses the development results of the AI- based CNC parameter identification and optimisation tool AICNC. The identification tool supports the AI-based optimisation of milling machine process parameters when using unknown material compositions. The process parameters are determined by a specific test pattern designed to be automatically analysed in real-time by a pre- trained perception-based deep learning algorithm. The tool provides the advantage of obtaining real-time quality information due to AI- based quality assessment and the automated identification of material- dependent milling process parameter sets, even for unknown processing material. </div> <br /> </div> <div id="articleSubject"> <h4>Keywords</h4> <br /> <div>Artificial Intelligence · CNC-Milling · Image Processing · Parameter Prediction · Process Optimisation</div> <br /> </div> <div id="articleCitations"> <h4>References</h4> <br /> <div> <p style="text-align: justify;">Sáenz de Argandona E., Aztiria A., Garcia C., AranaN., Izaguirre A., Fillatreau P. ’Forming processes control by means of artificial intelligence techniques’. Journal of Robotics and Computer-Integrated Manufacturing,2008, doi: 10.1016/j.rcim.2008.03.014.</p> <p style="text-align: justify;">Europäische Kommission, ’Mitteilung der Kommission an das Europäische Parlament, den Europäischen Rat, den Europäischen Wirtschafts- und Sozialausschuss und den Ausschuss der Regionen: Green Deal der Europäischen Union’, Brüssel, 2019. </p> <p style="text-align: justify;">Romano P., ’European Manufacture of the Future, role of research and education for E European leadership’, MANUFUTURE 2003 Conference, Italy, 2003.</p> <p style="text-align: justify;">Schlosser J. M., Schneider R., Rimkus W., Kelsch R., Gerstner F., Harrison D. K., Grant R. J., ’Material and simulation modelling of a crash beam performance – a comparison study showing the potential for weight savings using warm-formed ultra-high strength aluminium alloys’, Journal of Physics, 2017, Vol. 896, pp. 12091, DOI 10.1088/17426596/896/1/012091 </p> <p style="text-align: justify;">Feldmann S., Kempter G., Esslinger R., Tran H. T., ’Support of Image- Based Quality Assessment in Discrete Production Scenarios through AI- Based Decision Support’, 13th International Conference on Advancements in Computational Sciences, ICACTE, 2020, ISBN: 978-1- 4503-7732-4</p> <p style="text-align: justify;">Iandola F. N., Han S., Moskewicz M. W., Ashraf K., Dally W. J., Keutzer K., ’SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and ¡0.5MB model size, 2016, arXiv:1602.07 </p> <p style="text-align: justify;">[Online]. Available at: https://www.wirtschaft-digital-bw.de/ki-made-in- bw/innovationswettbewerb-ki-fuer-kmu/simki-echtzeitdatenerfassung- und-parameterkorrektur, 2020 </p> <p style="text-align: justify;">8. [Online] x-technik IT und Medien GmbH, ’DMU 65 monoBLOCK CNC universal milling machine’. [Online]. Available at: https: // https://www.zerspanungstechnik.com/bericht/horizontal- bearbeitungszentren/dmu-65-h-monoblock_mit-bearbeitungszentrum- maximal-flexibel-in-der-massenproduktion_2022-01-01, accessed in November 2022.</p> <p style="text-align: justify;">Hoffmann Group, Fraunhofer-Institut für Werkzeugmaschinen und Umformtechnik, ’Zerspanungshandbuch: Bohren, Gewinde, Senken, Reiben, Sägen, Fräsen, Drehen, Spanen, Präzisions-schleifen’, 2. Aufl. München: Hoffmann Group, 2014, ISBN: 3-00-016882-6</p> <p style="text-align: justify;">Landola, Forrest N., et al. "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5 MB model size." arXiv preprint arXiv:1602.07360 (2016).</p> <p style="text-align: justify;">Kingma, Diederik P., and Jimmy Ba. "Adam: A method for stochastic optimisation." arXiv preprint arXiv:1412.6980 (2014).</p> <p style="text-align: justify;">Rong W., Li Z., Zhang W., Sun L., An improved Canny edge detection algorithm, IEEE International Conference on Mechatronics and Automation, 2016, doi: 10.1109/ICMA.2014.6885761</p> <p style="text-align: justify;">Russakovsky, O., Deng, J., Su, H., et al. ’ImageNet LargeScale Visual Recognition Challenge’, International Journal of Computer Vision (IJCV). Vol 115, Issue 3, 2015, arXiv:1409.0575v3</p> <p style="text-align: justify;">Feldmann, M. Schmiedt, J. M. Schlosser, W. Rimkus, T. Stempfle, C. Rathmann, ’Recursive quality optimisation of a smart forming tool under the use of perception-based hybrid datasets for training of a Deep Neural Network’, Journal of Discover Artificial Intelligence, 2022, doi: 10.1007/s44163-022-00034-4 </p> </div> <br /> </div> Full Text: <a href="/issue/archive/papers/131.html" class="file" target="_parent">PDF</a> <div class="separator"></div> <h3>Refbacks</h3> <ul class="plain"> <li>There are currently no refbacks.</li> </ul> <br /><br /> <a target="_new" rel="license" href="http://creativecommons.org/licenses/by/3.0/"> <img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/3.0/80x15.png"/> </a> <br/> This work is licensed under a <a rel="license" target="_new" href="http://creativecommons.org/licenses/by/3.0/">Creative Commons Attribution 3.0 License</a>. <br /><br /> <p><img src="index_files/blocks_A1_Innovation.jpg" alt="IT in Innovation" width="207" height="46" /> <img src="index_files/blocks_A1_Business.jpg" alt="IT in Business" width="207" height="46" /> <img src="index_files/blocks_A1_Engineering.jpg" alt="IT in Engineering" width="207" height="46" /> <img src="index_files/blocks_A1_Health.jpg" alt="IT in Health" width="207" height="46" /> <img src="index_files/blocks_A1_Science.jpg" alt="IT in Science" width="207" height="46" /> <img src="index_files/blocks_A1_Design.jpg" alt="IT in Design" width="207" height="46" /> <img src="index_files/blocks_A1_Fashion.jpg" alt="IT in Fashion" width="207" height="46" /></p> IT in Industry @ <a href="http://www.it-in-industry.com">http://www.it-in-industry.com</a> . 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