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Knittel, Dominique
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (6/6 displayed)
- 2019Milling diagnosis using artificial intelligence approachescitations
- 2019Milling diagnosis using machine learning approaches
- 2018Honeycomb Core Milling Diagnosis using Machine Learning in the Industry 4.0 Frameworkcitations
- 2018Milling Diagnosis Using Machine Learning Techniques Toward Industry 4.0
- 2014Fabric friction behavior : study using capstan equation and introduction into a fabric transport simulatorcitations
- 2011New mathematical modelling and simulation of an industrial accumulator for elastic webscitations
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conferencepaper
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.