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Tool Wear State Recognition Based on One-Dimensional Convolutional Channel Attention.

Zhongling Xue1,2, Liang Li2, Ni Chen1,2

  • 1Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China.

Micromachines
|November 25, 2023
PubMed
Summary

This study introduces a new deep learning model, 1DCCA-CNN, for accurate tool wear state recognition in tool condition monitoring. The model enhances feature extraction, improving monitoring accuracy and preventing quality degradation.

Keywords:
1D Convolutionchannel attentiontool condition monitoringtool wear state recognition

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Tool condition monitoring (TCM) is crucial for efficient manufacturing, preventing premature tool replacement and ensuring workpiece quality.
  • Online tool wear recognition optimizes machining processes by providing real-time data on tool health.

Purpose of the Study:

  • To develop an improved deep learning model for accurate tool wear state recognition.
  • To enhance the performance of tool condition monitoring systems through advanced feature extraction.

Main Methods:

  • A convolutional neural network (CNN) was employed for deep feature extraction from preprocessed cutting signals.
  • A novel one-dimensional CNN (1DCNN)-based channel attention mechanism was integrated to enhance critical feature channels.
  • Time-domain features were extracted and combined into a new temporal sequence for analysis.

Main Results:

  • The proposed 1DCCA-CNN model demonstrated superior performance in tool wear recognition.
  • The model achieved performance improvements of 4% and 5% on the T1 and T3 datasets, respectively, surpassing existing research benchmarks.
  • The 1DCNN attention mechanism effectively enhanced information interaction between feature channels.

Conclusions:

  • The 1DCCA-CNN model offers a significant advancement in tool wear state recognition for TCM.
  • The proposed attention mechanism effectively captures key features, leading to improved monitoring accuracy.
  • This research contributes to more efficient and reliable manufacturing processes through enhanced tool condition monitoring.