An efficient deep learning prognostic model for remaining useful life estimation of high speed CNC milling machine cutters

Abstract

CNC machines are engaged in numerous industries, including critical ones like the aerospace, automotive, and military sectors, among others. Sensor data are time-series that may suffer from complex interconnections between variables and dynamic features. Long Short Term Memory LSTM excels in dynamic feature extraction, and Autoencoder AE has great capabilities in nonlinear deep knowledge of time-series data variables. In this work, we propose a model for tool wear prediction of CNC milling machine cutters as a type of time-series data taking advantage of the LSTM and AE capabilities. The framework consists of many steps, including extracting multi-domain features and a correlation analysis to select the most correlated features to the tool wear. New features are added, such as entropy and interquartile range IQR, which proved to be highly correlated to the cutter tool wear. An LSTM`-AE model is then trained, validated, and tested on this feature map to predict the target tool wear value. The model is provided with degradation or Run-To-Failure data for CNC machine cutters, the PHM10 dataset, to predict the tool wear values. The predicted tool wear value is compared against the wear curve to estimate RUL values. The predicted RUL values mostly underestimate the real values, which helps schedule for maintenance or equipment replacement before failure. The experimental results show that the proposed framework outperforms state-of-the-art DL methods in tool wear prediction accuracy approaching %98, as well as an enhancement of MAE and RMSE in the test set by reaching 2.6 ± 0.3222E-3 and 3.1 ± 0.6146 E-3, respectively.