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Honeycomb Core Milling Diagnosis using Machine Learning in the Industry 4.0 Framework

Abstract : In an industrial environment, data driven smart diagnosis of the milling is a difficult task. In this work, the diagnosis using machine learning techniques has been developed and implemented for composite sandwich structures based on honeycomb core. The objective is to predict online the resulting surface flatness. The time domain and frequency domain features are calculated from the measured milling forces and different algorithms have been implemented and compared. The experimental results have shown that a good milling diagnosis can be obtained with a Linear Support Vector Machine (SVM) algorithm resulting to good accuracy and short training time.
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https://hal.univ-lorraine.fr/hal-03325683
Contributor : Mohammed NOUARI Connect in order to contact the contributor
Submitted on : Wednesday, August 25, 2021 - 10:50:57 AM
Last modification on : Friday, August 5, 2022 - 2:54:00 PM

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Lorraine Codjo, Mohamed Jaafar, Hamid Makich, Dominique Knittel, Mohammed Nouari. Honeycomb Core Milling Diagnosis using Machine Learning in the Industry 4.0 Framework. 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), Sep 2018, Turin, France. pp.1251-1258, ⟨10.1109/ETFA.2018.8502475⟩. ⟨hal-03325683⟩

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