Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024) 2024
DOI: 10.1117/12.3032833
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Tool wear prediction based on hybrid feature selection

Wanzhen Wang,
Sze Song Ngu,
Miaomiao Xin
et al.

Abstract: Tool wear monitoring can provide information and improve productivity for tool change decisions in Computer Numerical Control (CNC) machine manufacturing. The processing of indirect signals and extracting sensitive features related to tool wear are critical. In this paper, a hybrid feature selection method is proposed and verified by improved Long Short-Term Memory (LSTM) models. Firstly, data preprocessing is performed on the acquired vibration, force, and acoustic emission (AE) signals and relevant features … Show more

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