Meta-learning-based approach for tool condition monitoring in multi-condition small sample scenarios-Reference-Cited by-同舟云学术

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Meta-learning-based approach for tool condition monitoring in multi-condition small sample scenarios

Published:2024-07 Issue: Volume:216 Page:111444
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Zhang Bowen^ORCID,Liu Xianli,Yue Caixu,Liang Steven Y.,Wang Lihui

Publisher

Elsevier BV

Reference40 articles.

1. An approach for tool wear prediction using customized DenseNet and GRU integrated model based on multi-sensor feature fusion;Liu;J. Intell. Manuf.,2023

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

3. An imbalanced data learning approach for tool wear monitoring based on data augmentation;Zhang;J. Intell. Manuf.,2023

4. On-line tool wear monitoring based on machine learning;Mu;J. Adv. Manufact. Sci. Technol.,2021

5. Adaptive weighted generative adversarial network with attention mechanism: a transfer data augmentation method for tool wear prediction;He;Mech. Syst. Sig. Process.,2024

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