Developing a deep learning-based uncertainty-aware tool wear prediction method using smartphone sensors for the turning process of Ti-6Al-4V

Abstract

Accurately predicting tool wear is crucial for intelligent machining process monitoring, control, and quality improvement. Recent studies on tool wear prediction predominantly apply deep learning-based data-driven approaches that use multivariate time-series signals from high-precision sensors. However, the reliance on these sensors incurs high installation and operation costs, posing practical challenges for small and mediumsized enterprises. This work proposes a novel deep learning-based approach that employs smartphone sensors to predict tool wear, which addresses the problems associated with smartphone sensor data, including higher noise levels and increased data and model uncertainties. To this end, this work develops various data-driven techniques for effective tool wear prediction and uncertainty quantification. First, a Kalman filter-based noise suppression method is applied to reduce undesired noise effects. Second, a novel uncertainty modeling method consisting of a Bayesian deep learning approach and a density output structure is proposed to capture both aleatoric and epistemic uncertainties during tool wear prediction. The proposed method not only takes into account high noise levels and induced uncertainty, but also continuously quantifies and dissects predictive uncertainty. The proposed method's effectiveness is validated with real-world datasets from Ti-6Al-4V turning experiments under three different machining conditions. The comprehensive experimental results indicate the superior prediction performance of the proposed method compared to existing data-driven methods, probabilistic deep learning-based methods, and state-of-the-art methods. For each of the three distinct datasets, the proposed method provides the lowest mean absolute error (MAE) values of 2.5815, 1.2414, and 1.2269, with the highest R2 2 values of 0.9951, 0.9971, and 0.9982, respectively.