【摘要】 刀具磨损预测对保证零件加工质量和效率、降低加工成本具有重要作用，尤其是在广泛采用难加工材料的航空航天制造领域。数据与机理融合模型能够结合机理模型和数据驱动模型的优势，是实现刀具磨损预测的有效手段。然而现有的融合方法难以有效平衡数据和机理对模型的权重，导致难以真正实现融合模型的预期效果。本文提出了一种基于元学习（Meta learning,ML）和PINN(Physics-informed neural network)的刀具磨损预测方法，通过磨损机理约束数据驱动模型的解空间，并结合元学习算法优化融合模型的损失函数以合理利用数据和机理提供的信息。实例验证结果表明，本文所提出的方法能有效提高变工况下的刀具磨损预测精度和稳定性。更多还原

【Abstract】 Tool wear prediction plays an important role in ensuring the machining quality and efficiency,and reducing the machining cost,especially in the aerospace manufacturing field where difficult machining materials are widely used. Data and mechanism fusion model can combine the advantages of mechanism model and data-driven model. It is an effective way to realize tool wear prediction. However,the existing fusion methods are difficult to effectively balance the weight of data and mechanism on the model,which makes it difficult to really achieve the expected effect of the fusion model. To solve the above problems,this paper proposes a tool wear prediction method based on meta learning(ML)and PINN(Physics-informed neural network). The solution space of the data-driven model is constrained by the wear mechanism,and the loss function of the fusion model is optimized by the meta learning algorithm to make rational use of the information provided by the data and mechanism. The experimental results show that the proposed method can effectively improve the accuracy and stability of tool wear prediction under varying cutting conditions.更多还原