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Related Experiment Video

Updated: Jun 27, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Wear Status Monitoring Method of Milling Cutter Under Variable Working Conditions Based on Transfer Learning and

Zhaohui Deng1, Zhiwu Liu1, Da Liu1

  • 1Institute of Manufacturing Engineering, Huaqiao University, Xiamen 361021, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel method for monitoring milling cutter wear status under varying conditions. The approach utilizes transfer learning and a lightweight SqueezeNet model, achieving high accuracy in wear recognition.

Area of Science:

  • Mechanical Engineering
  • Machine Learning
  • Signal Processing

Background:

  • Current tool wear monitoring models struggle with generalization due to varying signal characteristics across different processing conditions.
  • This limitation leads to insufficient accuracy in recognizing the tool's wear status.

Purpose of the Study:

  • To develop a robust method for monitoring milling cutter wear status under variable working conditions.
  • To improve the generalization and accuracy of wear status recognition models.

Main Methods:

  • Employed continuous wavelet transform (CWT) to convert raw vibration signals into time-frequency energy diagrams, preserving joint feature distributions.
  • Developed a monitoring model using a phased transfer learning strategy and the lightweight SqueezeNet architecture.
Keywords:
lightweight SqueezeNet modelmilling cuttertool wear status monitoringtransfer learningvariable working conditions

Related Experiment Videos

Last Updated: Jun 27, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

  • Validated the model through comparative experiments using vibration signals from different milling conditions.

    • Main Results:

    • The proposed transfer learning-based SqueezeNet model achieved a test set recognition accuracy of 94.583% under variable working conditions.
    • This accuracy surpasses the 91.133% achieved by the LSTM-DBO-SVM model.
    • The results demonstrate the model's effectiveness and feasibility for real-world applications.

    Conclusions:

    • The proposed method effectively addresses the challenge of insufficient model generalization in tool wear monitoring.
    • The combination of CWT, transfer learning, and SqueezeNet offers an accurate and adaptive solution for milling cutter wear status identification.
    • This approach significantly enhances the reliability of tool wear monitoring systems in dynamic manufacturing environments.