A Feature Selection Method for Machine Tool Wear Diagnosis

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Published Sep 4, 2023
Yuji Homma Takaaki Nakamura

Abstract

We propose an algorithm for estimating the wear condition of tools. We have previously developed a method for predicting machining dimensions by learning features of waveform shapes such as torque during machining as explanatory variables and measured machining dimensions as objective variables. In this method, the features do not fully explain the machining dimensions because including data other than the machining operation such as tool change. In this paper, we propose a method to improve explanatory power and prediction accuracy by selecting subsequences from the machining waveform that are highly related to machining dimensions as explanatory variables. The effectiveness of the proposed method was confirmed through an evaluation using data of machining product part.  

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Keywords

TIME SERIES, MACHINE TOOL, DATA ANALYSIS

References
Ke, Guolin, et al. "LightGBM: A highly efficient gradient boosting decision tree." Advances in Neural Information Processing Systems.

Mitsubishi Electric. Tetsushi Ishida & Takaaki Nakamura. "Machining dimension prediction device, machining dimension prediction system, machining dimension prediction method, and program." Japanese Patent Application No. 2022-503591. August 30, 2021.
Section
Special Session Papers