Physics-informed graph convolutional neural networks with weighted adaptive loss for tool wear prediction under variable working conditions
Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Published online on October 09, 2025
Abstract
Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Ahead of Print.
Tool wear prediction is crucial for enhancing safety in industrial production environments and improving product quality. However, data-driven models often fail to effectively capture local wear features, physics-based models typically neglect real-time ...
Tool wear prediction is crucial for enhancing safety in industrial production environments and improving product quality. However, data-driven models often fail to effectively capture local wear features, physics-based models typically neglect real-time ...