Multimodal Hidden Markov Model-Based Approach for Tool Wear Monitoring-Reference-Cited by-同舟云学术

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Multimodal Hidden Markov Model-Based Approach for Tool Wear Monitoring

Published:2014-06 Issue:6 Volume:61 Page:2900-2911
ISSN:0278-0046
Container-title:IEEE Transactions on Industrial Electronics
language:
Short-container-title:IEEE Trans. Ind. Electron.

Author:

Geramifard Omid,Xu Jian-Xin,Zhou Jun-Hong,Li Xiang

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/41/6690243/06566096.pdf?arnumber=6566096

Cited by 51 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. Efficient Tool Wear Prediction in Manufacturing: BiLPReS Hybrid Model with Performer Encoder;Arabian Journal for Science and Engineering;2024-04-15

2. Auxiliary input-enhanced siamese neural network: A robust tool wear prediction framework with improved feature extraction and generalization ability;Mechanical Systems and Signal Processing;2024-04

3. A novel method based on deep transfer learning for tool wear state prediction under cross-dataset;The International Journal of Advanced Manufacturing Technology;2024-01-30

4. A Computationally-efficient Semi-supervised Learning Model for the Estimation of State Degradation of a Milling Tool;2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI);2024-01-25

5. Tool wear classification based on maximal overlap discrete wavelet transform and hybrid deep learning model;The International Journal of Advanced Manufacturing Technology;2023-12-16

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