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Fast Automatized Parameter Adaption Process of CNC Milling Machines Under the Use of Perception Based Artificial Intelligence | S. Feldmann, M. Schmiedt, J. Jung, J. M. Schlosser, T. Stempfle, C. Rathmann, W. Rimkus | INFORMATION TECHNOLOGY IN INDUSTRY
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <link rel="icon" type="image/ico" href="https://it-in-industry.com/index_files/icon.gif"/> <title>Fast Automatized Parameter Adaption Process of CNC Milling Machines Under the Use of Perception Based Artificial Intelligence | S. Feldmann, M. Schmiedt, J. Jung, J. M. Schlosser, T. Stempfle, C. Rathmann, W. Rimkus | INFORMATION TECHNOLOGY IN INDUSTRY</title> <meta name="description" content="This paper concerns unpublished results obtained from the SIMKI (2020) R&D project at the Department of Mechanical Engineering at Aalen University of Applied Science, Germany.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 AIbased 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 pretrained perception-based deep learning algorithm. The tool provides the advantage of obtaining real-time quality information due to AIbased quality assessment and the automated identification of materialdependent milling process parameter sets, even for unknown processing material."> <meta name="keywords" content="Artificial Intelligence 路 CNC-Milling 路 Image Processing 路 Parameter Prediction 路 Process Optimisation"> <meta name="DC.Contributor.Sponsor" xml:lang="en" content=""> <meta name="DC.Creator.PersonalName" content="S. Feldmann"> <meta name="DC.Creator.PersonalName" content="M. Schmiedt"> <meta name="DC.Creator.PersonalName" content="J. Jung"> <meta name="DC.Creator.PersonalName" content="J. M. Schlosser"> <meta name="DC.Creator.PersonalName" content="T. Stempfle"> <meta name="DC.Creator.PersonalName" content="C. Rathmann"> <meta name="DC.Creator.PersonalName" content="W. Rimkus "> <meta name="DC.Date.created" scheme="ISO8601" content="2024-02-15"> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2024-02-15"> <meta name="DC.Date.issued" scheme="ISO8601" content="2024-02-15"> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-02-15"> <meta name="DC.Description" xml:lang="en" content="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 AIbased 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 pretrained perception-based deep learning algorithm. The tool provides the advantage of obtaining real-time quality information due to AIbased quality assessment and the automated identification of materialdependent milling process parameter sets, even for unknown processing material."> <meta name="DC.Format" scheme="IMT" content="application/pdf"> <meta name="DC.Identifier" content="131"> <meta name="DC.Identifier.pageNumber" content="09-21"> <meta name="DC.Identifier.URI" content="https://it-in-industry.com/issue/archive/131.html"> <meta name="DC.Language" scheme="ISO639-1" content="en"> <meta name="DC.Rights" content="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 AIbased 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 pretrained perception-based deep learning algorithm. The tool provides the advantage of obtaining real-time quality information due to AIbased quality assessment and the automated identification of materialdependent milling process parameter sets, even for unknown processing material."> <meta name="DC.Source" content="INFORMATION TECHNOLOGY IN INDUSTRY"> <meta name="DC.Source.ISSN" content="2203-1731"> <meta name="DC.Source.Issue" content="1"> <meta name="DC.Source.URI" content="http://www.it-in-industry.com"> <meta name="DC.Source.Volume" content="12"> <meta name="DC.Subject" xml:lang="en" content="Artificial Intelligence 路 CNC-Milling 路 Image Processing 路 Parameter Prediction 路 Process Optimisation"> <meta name="DC.Subject" xml:lang="en" content="Artificial Intelligence"> <meta name="DC.Subject" xml:lang="en" content="CNC-Milling, Parameter Prediction "> <meta name="DC.Title" content="Fast Automatized Parameter Adaption Process of CNC Milling Machines Under the Use of Perception Based Artificial Intelligence"> <meta name="DC.Type" content="Text.Serial.Journal"> <meta name="DC.Type.articleType" content="Peer-Reviewed Articles"> <meta name="gs_meta_revision" content="1.1"> <meta name="citation_journal_title" content="INFORMATION TECHNOLOGY IN INDUSTRY"> <meta name="citation_issn" content="2203-1731"> <meta name="citation_author" content="S. 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