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Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization.

Jiaqi Wang1, Zhong Xiang1, Xiao Cheng1,2

  • 1School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.

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|October 28, 2023
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Summary

This study introduces an improved northern goshawk optimization-support vector machine (INGO-SVM) model for accurate tool wear state identification. The INGO-SVM model achieved 97.9% accuracy in milling wear experiments, enhancing machining precision and reducing downtime.

Keywords:
improved northern goshawk optimizationrecursive feature eliminationsupport vector machinetool wear state identification

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Area of Science:

  • Manufacturing Engineering
  • Mechanical Engineering
  • Signal Processing

Background:

  • Tool wear significantly impacts equipment downtime and machining precision.
  • Accurate tool wear state identification is crucial for optimizing manufacturing processes.
  • Existing methods may lack the required accuracy and efficiency.

Purpose of the Study:

  • To develop a novel and highly accurate tool wear state identification technique.
  • To improve the efficiency and stability of tool wear monitoring systems.
  • To enhance machining precision and reduce operational costs.

Main Methods:

  • Wavelet packet thresholding denoising for multi-source signal processing and feature extraction.
  • Support Vector Machine Recursive Feature Elimination (SVM-RFE) for selecting relevant features.
  • An improved northern goshawk optimization (INGO) algorithm to optimize Support Vector Machine (SVM) parameters, forming the INGO-SVM model.

Main Results:

  • The INGO algorithm demonstrated superior convergence efficacy and stability in simulation tests.
  • The proposed INGO-SVM model achieved a remarkable recognition accuracy rate of 97.9% in milling wear experiments.
  • This approach outperformed five other comparative methods in terms of recognition accuracy.

Conclusions:

  • The INGO-SVM model offers a highly accurate and reliable solution for tool wear state identification.
  • This technique can significantly contribute to reducing equipment downtime and improving machining precision.
  • The study validates the effectiveness of combining advanced signal processing with optimized machine learning for industrial monitoring.