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Tool Health Condition Recognition Method for High Speed Milling of Titanium Alloy Based on Principal Component Analysis (PCA) and Long Short Term Memory (LSTM)

YANG Qirui XU Kaizhou ZHENG Xiaohu XIAO Lei BAO Jinsong

东华大学学报（英文版）2019，Vol.36Issue(4)：364-368,5.

Tool Health Condition Recognition Method for High Speed Milling of Titanium Alloy Based on Principal Component Analysis (PCA) and Long Short Term Memory (LSTM)

YANG Qirui ¹XU Kaizhou ²ZHENG Xiaohu ¹XIAO Lei ¹BAO Jinsong¹

作者信息

1. College of Mechanical Engineering, Donghua University, Shanghai 201600, China
2. Shanghai Space Propulsion Technology Research Institute, Shanghai 201100, China
折叠

摘要

关键词

health condition recognition/milling tool/principal component analysis(PCA)/long short term memory, (LSTM)

Key words

health condition recognition/milling tool/principal component analysis(PCA)/long short term memory, (LSTM)

分类

信息技术与安全科学

引用本文复制引用

YANG Qirui,XU Kaizhou,ZHENG Xiaohu,XIAO Lei,BAO Jinsong..Tool Health Condition Recognition Method for High Speed Milling of Titanium Alloy Based on Principal Component Analysis (PCA) and Long Short Term Memory (LSTM)[J].东华大学学报（英文版）,2019,36(4):364-368,5.

基金项目

National Natural Science Foundation of China (No.51805079) （No.51805079）

Shanghai Natural Science Foundation,China (No.17ZR1400600) （No.17ZR1400600）

Fundamental Research Funds for the Central Universities,China (No.16D110309) （No.16D110309）

东华大学学报（英文版）

ISSN：1672-5220

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