Research on Tool Wear Prediction Based on LSTM and ARIMA-Reference-Cited by-同舟云学术

Research on Tool Wear Prediction Based on LSTM and ARIMA

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 International Conference on Big Data Engineering and Technology - BDET 2018
language:
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Author:

Zhang Zhenkun¹,Lu Juan²,Zhou Gang¹,Liao Xiaoping¹

Affiliation:

1. College of Mechanical & Engineering, Guangxi University, Nanning, Guangxi, China

2. Department of Mechanical & Engineering, Qinzhou University, Qinzhou, Guangxi, China

Funder

Guangxi Colleges and Universities Key Laboratory Breeding Base of Coastal Mechanical Equipment Design, Manufacturing and Control

National Natural Science Foundation of China

Innovation Project of Guangxi Graduate Education

Publisher

ACM Press

Reference30 articles.

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4. Zhou, Z. D., Chen, Y. P., Fuh, J. Y. H., and Nee, A. Y. C. 2000. Integrated condition monitoring and fault diagnosis for modern manufacturing systems. CIRP Annals-Manufacturing Technology, 49(1), 387--390. DOI= https://doi.org/10.1016/S0007--8506(07)62971--0

5. Kurada, S., and Bradley, C. 1997. A review of machine vision sensors for tool condition monitoring. Computers in industry, 34(1), 55--72. DOI= https://doi.org/10.1016/S0166--3615(96)00075--9

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