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A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural

Xu Yang1,2, Rui Yuan1,2, Yong Lv1,2

  • 1Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China.

Sensors (Basel, Switzerland)
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for monitoring tool wear using cutting force signals and a one-dimensional convolutional neural network (1D CNN). The approach accurately detects tool wear conditions in precision manufacturing.

Keywords:
modified multiscale permutation entropymultivariate cutting force signalsone-dimensional convolutional neural networktool wear condition monitoring

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Area of Science:

  • Manufacturing Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Tool wear monitoring is critical in precision manufacturing for maintaining product quality and process efficiency.
  • Cutting force signals contain valuable dynamic information about tool wear states.
  • Existing methods may struggle with the nonlinear and nonstationary characteristics of tool wear signals.

Purpose of the Study:

  • To propose a novel multivariate cutting force-based method for tool wear condition monitoring.
  • To leverage the power of one-dimensional convolutional neural networks (1D CNN) for enhanced monitoring accuracy and stability.
  • To develop a robust system for real-time tool wear assessment in machining processes.

Main Methods:

  • Multivariate variational mode decomposition (MVMD) was applied to process multivariate cutting force signals, extracting multivariate band-limited intrinsic mode functions (BLIMFs).
  • Modified multiscale permutation entropy (MMPE) was utilized to quantify the complexity and extract condition indicators from the BLIMFs.
  • One-dimensional feature vectors were constructed from the entropy values and fed into a 1D CNN for classification of tool wear conditions.

Main Results:

  • The MVMD successfully decomposed cutting force signals into informative BLIMFs, capturing nonlinear and nonstationary tool wear characteristics.
  • MMPE effectively generated condition indicators reflecting the complexity of the extracted BLIMFs across multiple scales.
  • The 1D CNN model achieved accurate and stable tool wear condition monitoring using the derived feature vectors.

Conclusions:

  • The proposed method, integrating MVMD, MMPE, and 1D CNN, offers a promising approach for effective tool wear monitoring.
  • This technique demonstrates significant potential for improving precision manufacturing through reliable condition monitoring.
  • The developed methodology provides a robust framework for analyzing complex cutting force dynamics for tool wear assessment.