Extending cutting tool remaining life through deep learning and laser shock peening remanufacturing techniques

Abstract

Predicting and extending the remaining life of cutting tools during machining processes is crucial for sustainable manufacturing. Traditional prognosis methods often struggle to adapt to diverse working conditions throughout the machining process lifecycle. This paper introduced a comprehensive framework that effectively addressed the challenges by integrating multi-source data and using deep learning techniques. The system integrated power and vibration data collected from LGMazak VTC-16A and IRON MAN QM200 machines with the following innovations: (1) A standardized data fusion method was developed to integrate multi-source data sources, the hybrid graph convolutional network (GCN) with attention mechanisms was employed to improve the prognosis accuracy of cutting tool remaining life, best accuracy of 98.56% and average accuracy of 97.71% were achieved. (2) The optimization of laser shock peening (LSP) remanufacturing parameters using the bees algorithm showed good performance, a fitness value of 0.95 was achieved with convergence within 15 iterations. (3) Monitoring of the LSP remanufacturing process was designed based on sound and vibration data for optimal remanufacturing performance. (4) The remanufacturing approach in extending the remaining life of cutting tool was validated through FEA analysis and experimental testing, cutting tool life was extended by 29.32% to achieve a sustainable manufacturing process.