Tool Wear Status Recognition and Prediction Model of Milling Cutter Based on Deep Learning

Tool Wear Status Recognition and Prediction Model of Milling Cutter Based on Deep Learning

In order to ensure the reliability and stability of the manufacturing process, tool wear state should be realized real-time and accurate monitoring. This paper proposes a tool wear state recognize and predictive framework model based on Stacking Sparse De-noising Auto-Encoder (SSDAE), the Particle S...

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Bibliographic Details
Main Authors: Yang Xie, Chaoyong Zhang, Qiong Liu
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Tool condition monitoring
deep learning
SSDAE
feature fusion
PSO-LSSVM algorithm
Online Access:https://ieeexplore.ieee.org/document/9306825/

Internet

https://ieeexplore.ieee.org/document/9306825/

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