Application of Weighted Combination Model in Tool Wear State Identification
Author:
Affiliation:
1. Hunan International Economics University,College of Information and Mechatronical Engineering,Changsha,China,410205
2. TBM Research and Design Institute,China Railway Construction Heavy Industry Corporation Limited,Changsha,China,410199
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10143535/10143504/10143552.pdf?arnumber=10143552
Reference10 articles.
1. Time varying and condition adaptive hidden Markov model for tool wear state estimation and remaining useful life prediction in micro-milling;weijian;Mechanical Systems and Signal Processing,2019
2. A tool wear monitoring and prediction system based on multiscale deep learning models and fog computing;huihui;International Journal of Advanced Manufacturing Technology,2020
3. A hybrid information model based on long short-term memory network for tool condition monitoring Journal of Intelligent Manufacturing;weili,2020
4. Applying the selforganization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting;chia-liang;Mechanical Systems and Signal Processing,2013
5. A generic tool wear model and its application to force modeling and wear monitoring in high speed milling;kunpeng;Mechanical Systems and Signal Processing,2019
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