An Augmented AutoEncoder With Multi-Head Attention for Tool Wear Prediction in Smart Manufacturing
Author:
Affiliation:
1. School of Computer Engineering, Weifang University, Weifang, China
2. School of Physics and Electronic Information, Weifang University, Weifang, China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10541951.pdf?arnumber=10541951
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1. An improved feature selection method based on random forest algorithm for wind turbine condition monitoring;Li;Sensors,2021
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3. An improved ResNet-1D with channel attention for tool wear monitor in smart manufacturing;Dong;Sensors,2023
4. Tool condition monitoring techniques in milling process—A review;Mohanraj;J. Mater. Res. Technol.,2020
5. Overview of tool wear monitoring methods based on convolutional neural network;Wang;Appl. Sci.,2021
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