A new time–space attention mechanism driven multi-feature fusion method for tool wear monitoring-Reference-Cited by-同舟云学术

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A new time–space attention mechanism driven multi-feature fusion method for tool wear monitoring

Published:2022-03-21 Issue:7-8 Volume:120 Page:5633-5648
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Feng Tingting,Guo Liang,Gao Hongli,Chen Tao,Yu Yaoxiang,Li Changgen

Funder

National Natural Science Foundation of China

Local Development Foundation guided by the Central Government

Planning Project of Science & Technology Department of Sichuan Province under Grant

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00170-022-09032-3.pdf

Reference37 articles.

1. Liu Y, Guo L, Gao H, You Z, Ye Y, Zhang B (2022) Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: a review. Mech Syst Signal Process 164:108068. https://doi.org/10.1016/j.ymssp.2021.108068

2. Wang Y, Zheng L, Wang Y (2021) Event-driven tool condition monitoring methodology considering tool life prediction based on industrial internet. J Manuf Syst 58:205–222. https://doi.org/10.1016/j.jmsy.2020.11.019

3. Liao Z, Gao D, Lu Y, Lv Z (2016) Multi-scale hybrid HMM for tool wear condition monitoring. Int J Adv Manuf Technol 84:2437–2448. https://doi.org/10.1007/s00170-015-7895-3

4. You Z, Gao H, Guo L, Liu Y, Li J (2020) On-line milling cutter wear monitoring in a wide field-of-view camera. Wear 460–461:203479. https://doi.org/10.1016/j.wear.2020.203479

5. Yang Y, Guo Y, Huang Z, Chen N, Li L, Jiang Y, He N (2019) Research on the milling tool wear and life prediction by establishing an integrated predictive model. Meas J Int Meas Confed 145:178–189. https://doi.org/10.1016/j.measurement.2019.05.009

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2. Efficient Tool Wear Prediction in Manufacturing: BiLPReS Hybrid Model with Performer Encoder;Arabian Journal for Science and Engineering;2024-04-15

3. Tool wear state recognition and prediction method based on laplacian eigenmap with ensemble learning model;Advanced Engineering Informatics;2024-04

4. Data-model linkage prediction of tool remaining useful life based on deep feature fusion and Wiener process;Journal of Manufacturing Systems;2024-04

5. A Novel Piecewise Cubic Hermite Interpolating Polynomial-Enhanced Convolutional Gated Recurrent Method under Multiple Sensor Feature Fusion for Tool Wear Prediction;Sensors;2024-02-08

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