Intelligent Tool Condition Monitoring Based on Multi-Scale Convolutional Recurrent Neural Network

Xincheng CAO; Bin YAO; Binqiang CHEN; Wangpeng HE; Suqin GUO; Kun CHEN

doi:10.1587/transinf.2022DLP0043

Special Section on Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications

Intelligent Tool Condition Monitoring Based on Multi-Scale Convolutional Recurrent Neural Network

Xincheng CAO, Bin YAO, Binqiang CHEN, Wangpeng HE, Suqin GUO, Kun CHEN

Author information

Keywords: digital manufacturing, tool condition monitoring, acoustic, deep learning, convolutional recurrent neural network

JOURNAL FREE ACCESS

2023 Volume E106.D Issue 5 Pages 644-652

DOI https://doi.org/10.1587/transinf.2022DLP0043

Details

Abstract

Tool condition monitoring is one of the core tasks of intelligent manufacturing in digital workshop. This paper presents an intelligent recognize method of tool condition based on deep learning. First, the industrial microphone is used to collect the acoustic signal during machining; then, a central fractal decomposition algorithm is proposed to extract sensitive information; finally, the multi-scale convolutional recurrent neural network is used for deep feature extraction and pattern recognition. The multi-process milling experiments proved that the proposed method is superior to the existing methods, and the recognition accuracy reached 88%.

Corresponding author

Register with J-STAGE for free!