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Prediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM.

Zhang, X., Lu, X., Li, W. and Wang, S., 2021. Prediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM. The International Journal of Advanced Manufacturing Technology, 112, 2277 - 2299.

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DOI: 10.1007/s00170-020-06447-8

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.

Item Type:Article
ISSN:0268-3768
Uncontrolled Keywords:cutting tool life; Hurst exponent; CNN-LSTM; deep learning
Group:Faculty of Science & Technology
ID Code:35339
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:14 Apr 2021 10:30
Last Modified:15 Aug 2021 08:28

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