Milling Tool Wear Prediction Method Based on Deep Learning Under Variable Working Conditions-Reference-Cited by-同舟云学术

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Milling Tool Wear Prediction Method Based on Deep Learning Under Variable Working Conditions

Published:2020 Issue: Volume:8 Page:140726-140735
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Wang Mingwei,Zhou Jingtao^ORCID,Gao Jing^ORCID,Li Ziqiu^ORCID,Li Enming

Funder

National Key Research and Development Program of China

Shanghai Aerospace Innovation Technology Fund program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/8948470/09144206.pdf?arnumber=9144206

Cited by 53 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A tool wear monitoring method based on data-driven and physical output;Robotics and Computer-Integrated Manufacturing;2025-02

2. Milling wear prediction using an artificial neural network model;Engineering Applications of Artificial Intelligence;2024-09

3. A systematic multi-layer cognitive model for intelligent machine tool;Journal of Intelligent Manufacturing;2024-08-30

4. Tool Condition Monitoring in the Milling Process Using Deep Learning and Reinforcement Learning;Journal of Sensor and Actuator Networks;2024-07-30

5. Enhanced tool condition monitoring using wavelet transform-based hybrid deep learning based on sensor signal and vision system;The International Journal of Advanced Manufacturing Technology;2024-04-29

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