Computer numerical control machine tool wear monitoring through a data-driven approach
F Gougam,
A Afia,
MA Aitchikh
et al.
Abstract:The susceptibility of tools in Computer Numerical Control (CNC) machines makes them the most vulnerable elements in milling processes. The final product quality and the operations safety are directly influenced by the wear condition. To address this issue, the present paper introduces a hybrid approach incorporating feature extraction and optimized machine learning algorithms for tool wear prediction. The approach involves extracting a set of features from time-series signals obtained during the milling proces… Show more
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