Physics-informed graph convolutional neural networks with weighted adaptive loss for tool wear prediction under variable working conditions

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 environmental fluctuations, and current fusion methods struggle to optimally balance the contributions of physics-based and data-driven components. To address these limitations, this paper introduces a novel physics-informed graph convolutional neural network (PGCN-WL) for tool wear prediction. This method incorporates a graph convolutional neural network (GCN), where extracted features serve as nodes in an undirected graph structure. GCN facilitates the continuous aggregation of feature information from neighboring nodes, iteratively refining node features to enhance the model’s sensitivity to local wear dynamics within the input signal. Furthermore, a multi-layer perceptron learns an adaptive loss function designed to complement the physics-informed loss term. This component specifically captures the dynamic evolution of cutting parameters during the wear process. An adaptive weight update mechanism is also proposed to dynamically adjust the influence of different loss functions, thereby improving overall prediction accuracy. Finally, the Model-Agnostic Meta-Learning (MAML) framework is applied. It treats data originating from distinct operational conditions as separate learning tasks, enabling the model to acquire versatile internal representations that can adapt swiftly to various working environment. The efficacy of this combined approach is validated using a publicly available tool wear prediction dataset, demonstrating its effectiveness.