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Published in: The International Journal of Advanced Manufacturing Technology 9-12/2019

01-06-2019 | ORIGINAL ARTICLE

Tool life prediction based on Gauss importance resampling particle filter

Authors: Hua An, Guofeng Wang, Yi Dong, Kai Yang, Lingling Sang

Published in: The International Journal of Advanced Manufacturing Technology | Issue 9-12/2019

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Abstract

Effective tool remaining useful life (RUL) prediction can greatly improve the quality of workpiece and reduce the cost of production. An online tool RUL prediction framework is constructed based on Bayesian inference and sensory signals. In this method, Pairs model is adopted to depict the tool degradation process during cutting process and Gaussian importance resampling (GIR) method is proposed to update model parameters iteratively. Therefore, the future tool wear status can be predicted and RUL can be estimated correspondingly. To testify the effectiveness of the proposed method, milling experiment was carried out and relative waveform features are extracted to depict the relationship between sensory signal and tool wear value. The analysis and comparison show that Gaussian importance resampling method can avoid the problem of particle degradation and impoverishment effectively so as to realize accurate RUL prediction.

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Metadata
Title
Tool life prediction based on Gauss importance resampling particle filter
Authors
Hua An
Guofeng Wang
Yi Dong
Kai Yang
Lingling Sang
Publication date
01-06-2019
Publisher
Springer London
Published in
The International Journal of Advanced Manufacturing Technology / Issue 9-12/2019
Print ISSN: 0268-3768
Electronic ISSN: 1433-3015
DOI
https://doi.org/10.1007/s00170-019-03934-5

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