{"id":3110,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3110"},"modified":"2022-07-26T13:04:27","modified_gmt":"2022-07-26T11:04:27","slug":"modeling-of-deep-learning-applications-for-chatter-detection-in-the-milling-process","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/cutting-technologies-2\/modeling-of-deep-learning-applications-for-chatter-detection-in-the-milling-process\/","title":{"rendered":"Modeling of deep-learning applications for chatter detection in the milling process"},"content":{"rendered":"\n

by Khairul Jauhari, Ahmad Zaki Rahman, Mahfudz Alhuda, Muizuddin Azka, Achmad Widodo, Toni Prahasto, Keiji Yamada (Indonesia)<\/em><\/p>\n\n\n\n

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

This paper presents a small part of the study about the development of the digital twin model of milling machining process for detection chatter phenomenon. Chatter is a dynamic interaction where there is an unstable condition in the material removal process between the cutting tool and work-piece, so it affects the surface roughness, tool life which ultimately reduces the quality of machining results. Our goal is to develop a chatter detection model using deep learning application that can identify stable or unstable chatter. In this study, the model is built based on the data driven method where measured vibration signal data for the milling process is trained and tested using several supervised deep-learning methods. It is obtained a model with a good level of accuracy. Using a chatter detection application, regular operator staff could screen for machining conditions when no specialist is available.<\/p>\n\n\n\n

Keywords<\/strong>: Chatter; Digital Twin; Data-driven; Deep-Learning; Milling<\/p>\n\n\n\n

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