Prediction of the Remaining Useful Life of Cutting Tool using the Hurst Exponent and CNN-LSTM

Xiaoyang Zhang, Xin Lu, Weidong Li, Sheng Wang

Research output: Contribution to journalArticlepeer-review

Abstract

To enhance production quality, productivity and energy consumption, it is paramount to predict the remaining useful life (RUL) of a cutting tool accurately and efficiently. Deep learning algorithm-driven approaches have been actively explored in the research field though there are still potential areas to further enhance the performance of the approaches. In this research, to improve accuracy and expedite computational efficiency for predicting the RUL of cutting tools, a novel systematic methodology is designed to integrate strategies of signal partition and deep learning for effectively processing and analysing multi-sourced sensor signals collected throughout the lifecycle of a cutting tool. In more detail, the methodology consists of two sub-systems: (i) a Hurst exponent–based method is developed to effectively partition complex and multi-sourced signals along the tool wear evolution, and (ii) a hybrid CNN-LSTM algorithm is designed to combine feature extraction, fusion and regression in a systematic means to facilitate the prediction based on segmented signals. The system was validated using a case study with a large set of databases with multiple cutting tools and multi-sourced signals. Comprehensive comparisons between the proposed methodology and some other mainstream algorithms, such as CNN, LSTM, DNN and PCA, were carried out under the conditions of partitioned and unpartitioned signals. Benchmarks showed that, based on the case study in this research, the prediction accuracy of the proposed methodology reached 87.3%, which is significantly better than those of the comparative algorithms.

Original languageEnglish
Article numberhttps://doi.org/10.1007/s00170-020-06447-8
Pages (from-to)2277-2299
Number of pages23
JournalThe International Journal of Advanced Manufacturing Technology
Volume112
Early online date7 Jan 2021
DOIs
Publication statusPublished - Feb 2021

Bibliographical note

The final publication is available at Springer via http://dx.doi.org/10.1007/s00170-020-06447-8

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Keywords

  • CNN-LSTM
  • Cutting tool life
  • Deep learning
  • Hurst exponent

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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