{"id":3358,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3358"},"modified":"2022-07-26T13:03:54","modified_gmt":"2022-07-26T11:03:54","slug":"boundary-conditions-for-the-application-of-machine-learning-based-monitoring-systems-for-supervised-anomaly-detection-in-machining","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/cutting-technologies-2\/boundary-conditions-for-the-application-of-machine-learning-based-monitoring-systems-for-supervised-anomaly-detection-in-machining\/","title":{"rendered":"Boundary conditions for the application of machine learning based monitoring systems for supervised anomaly detection in machining"},"content":{"rendered":"\n

by <\/em>Berend Denkena, M. Wichmann, Hendrik Noske, D. Stoppel (Germany)<\/p>\n\n\n\n

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Abstract<\/strong><\/p>\n\n\n\n

Monitoring systems may contribute increasing the availability of machine tools and detecting process deviations in time. In the past, machine learning has been used to solve a variety of monitoring problems in machining. However, boundary conditions for the assessment of the principal applicability of machine learning approaches for supervised anomaly detection in machining have not been exhaustively described in the literature. In this paper, objectives as well as deficits of literature approaches are identified and influencing factors on the monitoring quality are described. As a result, we derive boundary conditions and discuss challenges for successful implementation of machine learning based monitoring systems for supervised anomaly detection in industrial practice.<\/p>\n\n\n\n

Keywords<\/strong>: Machine learning; Machining; Monitoring; Quality assurance<\/p>\n\n\n\n

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Video presentation<\/strong><\/p>\n\n\n\n