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Integrating physics-informed recurrent Gaussian process regression into instance transfer for predicting tool wear in milling process

  • Published:2023-06 Issue: Volume:68 Page:42-55
  • ISSN:0278-6125
  • Container-title:Journal of Manufacturing Systems
  • language:en
  • Short-container-title:Journal of Manufacturing Systems

Author:

Qiang Biyao,Shi Kaining,Liu Ning,Ren Junxue,Shi Yaoyao

Funder

National Natural Science Foundation of China

National Major Science and Technology Projects of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Hardware and Architecture,Software,Control and Systems Engineering

Reference31 articles.

1. Research on tool wear morphology and mechanism during turning nickel-based alloy GH4169 with PVD-TiAlN coated carbide tool;Liang;Wear,2022

2. Research on asymmetrical edge tool wear prediction in milling TC4 titanium alloy using deep learning;Yang;Measurement,2022

3. Physical model-based tool wear and breakage monitoring in milling process;Zhang;Mech Syst Signal Process,2023

4. Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders;He;Measurement,2022

5. Tool wear prediction method based on symmetrized dot pattern and multi-covariance Gaussian process regression;Zhang;Measurement,2022

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