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Milling Diagnosis Using Machine Learning Techniques Toward Industry 4.0

Abstract : Smart diagnosis of the milling in an industrial environment 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 goal is to qualify the resulting surface flatness. Different algorithms have been implemented and compared. The time domain and frequency domain features are calculated from the measured milling forces. The experimental results have shown that a good milling diagnosis can be obtained with a Linear Support Vector Machine (SVM) algorithm: good accuracy and short training time.
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Submitted on : Wednesday, August 25, 2021 - 11:00:47 AM
Last modification on : Friday, August 5, 2022 - 2:54:00 PM
Long-term archiving on: : Friday, November 26, 2021 - 7:51:06 PM


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  • HAL Id : hal-03325696, version 1


Lorraine Codjo, Mohamed Jaafar, Hamid Makich, Dominique Knittel, Mohammed Nouari. Milling Diagnosis Using Machine Learning Techniques Toward Industry 4.0. DX@ Safeprocess, Aug 2018, Lyon, France. ⟨hal-03325696⟩



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