A multi-input parallel convolutional attention network for tool wear monitoring
Author:
Affiliation:
1. Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin, China
2. Postdoctoral Mobile Station of Instrument Science and Technology, Harbin University of Science and Technology, Harbin, China
Funder
Natural Science Outstanding Youth Fund of Heilongjiang Province
Heilongjiang Province General Undergraduate Colleges and Universities Young Innovative Talents Training Plan
Manufacturing Science and Technology Innovation Talents Project of Harbin City
Publisher
Informa UK Limited
Subject
Electrical and Electronic Engineering,Computer Science Applications,Mechanical Engineering,Aerospace Engineering
Link
https://www.tandfonline.com/doi/pdf/10.1080/0951192X.2023.2294440
Reference31 articles.
1. Identification and modeling of cutting forces in ball-end milling based on two different finite element models with Arbitrary Lagrangian Eulerian technique
2. Analysis of flat-end milling forces considering chip formation process in high-speed cutting of Ti6Al4V titanium alloy
3. Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification
4. Intelligent tool wear monitoring and multi-step prediction based on deep learning model
5. An online belt wear monitoring method for abrasive belt grinding under varying grinding parameters
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