An uncertainty-aware deep learning ensemble approach for effective cutting tool predictive maintenance decision-making

Abstract As the manufacturing sector increasingly emphasizes production efficiency and product quality, predicting cutting tool remaining useful life (RUL) and conducting failure analysis become essential for ensuring continuity and reducing costs. However, the uncertainties in the tool wear process complicate accurate predictions and decision-making. This paper introduces a predictive maintenance decision-making approach using an ensemble model of convolutional neural network and bidirectional long short-term memory quantile regression (CNN-BiLSTMQR), enhanced by an attention mechanism and kernel density estimation (KDE). Firstly, signals related to tool wear are collected from various sensors, and a CNN captures spatial features and local patterns in the data. These features are then input into a BiLSTM network, which integrates a temporal attention mechanism and QR to predict RULs at multiple quantiles. Subsequently, KDE generates the probability density distribution of the tool RULs. Finally, the costs and time associated with maintenance decisions are assessed to derive cost functions for tool replacement and ordering. By minimizing the two functions, the optimal timing for tool replacement and spare parts procurement is identified. Validation with a publicly available tool wear prediction dataset demonstrates that the proposed method effectively reduces maintenance costs while ensuring the safe and reliable operation of tools.