CNN-LSTM enabled prediction of remaining useful life of cutting tool

o 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 systemic methodology is designed to integrate strategies of signal partition and deep learning for effectively processing and analyzing multi-sourced sensor signals throughout the lifecycle of a cutting tool. In more detail, the methodology consists of two subsystems: (i) a Hurst exponent-based method is developed to effectively partition complex and multi-sourced signals along the tool wear evolution; (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 is validated using a case study with a large set of databases using multiple cutting tools and with multi-sourced signals. Comprehensive comparisons between the proposed methodology and some other main-stream algorithms, such as CNN, LSTM, DNN and PCA, were carried out under the conditions of partitioned and un-partitioned signals. Benchmarks show that, based on the case study in this research, the prediction accuracy of the proposed methodology reached 87.3%, which are significantly better than those of the comparative algorithms.