Abstract Tool wear prediction is of significance to improve the safety and reliability of machining tools, given their widespread applications in nearly every branch of manufacturing. Mathematical modelling, including data driven modelling and physics-based modelling, is an important tool to predict the degree of tool wear. Howerver, the performance of conventional data driven models is restricted by the absent representation of physical inconsistency. The physics-based models usually fail to consider the complex tool cutting conditions and dynamic changes of physical parameters in practice. To address these issues, a novel physics guided neural network model is presented for tool wear prediction. Firstly, a cross physics-data fusion (CPDF) scheme is proposed as the modelling strategy to fuse the hidden information explored by a physics-based model and a data driven model. Secondly, the information hidden in the unlabelled sample is explored by the physics-based model of tool cutting, inspired by semi-supervised learning. Thirdly, a novel loss function which takes the physical discipline into account is proposed to evaluate the physical inconsistency quantitatively. The advantage of the developed method is that it explores sufficient information from both physics and data domains to eliminate the physical inconsistency existing in conventional data driven models.

中文翻译：

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用于加工刀具磨损预测的物理引导神经网络

摘要 刀具磨损预测对于提高加工刀具的安全性和可靠性具有重要意义，因为刀具磨损预测几乎在制造的每个分支中都有广泛的应用。数学建模，包括数据驱动建模和基于物理的建模，是预测刀具磨损程度的重要工具。然而，传统数据驱动模型的性能受到缺乏物理不一致表示的限制。基于物理的模型在实践中通常没有考虑复杂的刀具切削条件和物理参数的动态变化。为了解决这些问题，提出了一种用于刀具磨损预测的新型物理引导神经网络模型。首先，提出了一种交叉物理数据融合（CPDF）方案作为建模策略，以融合基于物理模型和数据驱动模型探索的隐藏信息。其次，在半监督学习的启发下，通过基于物理的刀具切削模型探索隐藏在未标记样本中的信息。第三，提出了一种新的考虑物理学科的损失函数来定量评估物理不一致性。所开发方法的优势在于它从物理和数据域中探索了足够的信息，以消除传统数据驱动模型中存在的物理不一致。提出了一种将物理学科考虑在内的新型损失函数来定量评估物理不一致性。所开发方法的优势在于它从物理和数据域中探索了足够的信息，以消除传统数据驱动模型中存在的物理不一致。提出了一种将物理学科考虑在内的新型损失函数来定量评估物理不一致性。所开发方法的优势在于它从物理和数据域中探索了足够的信息，以消除传统数据驱动模型中存在的物理不一致。