Cutting tool life prediction and extension through generative model-augmented deep learning and laser remanufacturing techniques

Predicting and extending the remaining life of cutting tools during machining processes is essential for sustainable manufacturing. Traditional prognosis methods often struggle to adapt to different working conditions over the machining process lifecycle. This paper proposes a novel framework that effectively addresses the challenges by integrating multi-source data and using deep learning techniques. The system integrates augmented-power and vibration data collected from computer numerical control machines with the following innovations: (1) A hybrid temporal convolutional network (TCN)-attention model is developed for cutting tool remaining life prognosis, which achieves the best accuracy of 98.51 % and average of 97.62 %. In addition, optimal laser shock peening parameters are selected using a deep neural network and enhanced ternary bees algorithm. (2) A time-series generative adversarial network is used for data augmentation, which increases data quantity for TCN model training. (3) Data quality is evaluated using the t-distributed stochastic neighbor embedding, Fréchet inception distance, and root mean squared error to ensure similarity between real and generated data. (4) The effectiveness of the remanufacturing approach is validated with a 28.95 % and 30.77 % increase in tool life based on finite element analysis and experimental testing, respectively. This comprehensive approach contributes to enhancing tool life prediction accuracy and optimizing sustainable remanufacturing processes, thereby enhancing production efficiency and reducing waste in machining operations.