Tool wear monitoring (TWM) is essential for enhancing the machining accuracy of intelligent manufacturing systems and ensuring the consistency and reliability of products. The complex and dynamic processing environment demands higher real-time monitoring and generalization ability of TWM. Traditional data-driven models lack guided training in physical processes and are limited by the amount of samples with wear labels. To guide the model to capture the underlying physical mechanism and enhance compliance with the law of tool wear, a dual knowledge embedded hybrid model based on augmented data and improved loss function for TWM is proposed in this paper. The second training data source is obtained by constructing the mapping relationship between cutting force and tool wear, which effectively complements and enhances the physical characteristics between the data and addresses the issue of insufficient labeled data in actual network training. Subsequently, a structure integrating serial convolution, parallel convolution, bidirectional gated recurrent unit (BiGRU) and attention mechanism is developed to extract the spatial and temporal features in time series data. Moreover, Based on the physical law of tool wear, an improved loss function with physical constraints is proposed to improve the physical consistency of the model. The experimental results show that the model prediction RMSE error is reduced by 12.67% after augmented data compared to a single data source, and the RMSE error of the prediction result is reduced by 25.16% at most after the improvement of the loss function. The model has high prediction accuracy within short training epochs and good real-time performance. The proposed approach provides a modeling strategy with low computational resource requirements based on the fusion of physical and data information.

中文翻译：

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基于增强数据和改进损失函数的双知识嵌入式混合模型，用于刀具磨损监测


刀具磨损监测 （TWM） 对于提高智能制造系统的加工精度和确保产品的一致性和可靠性至关重要。复杂多变的处理环境对 TWM 的实时监控和泛化能力提出了更高的要求。传统的数据驱动模型缺乏物理过程的指导训练，并且受到带有磨损标签的样本数量的限制。为了指导模型捕捉底层物理机制并增强对工具磨损定律的顺应性，本文提出了一种基于增强数据和改进损失函数的双知识嵌入式混合模型。通过构建切削力与刀具磨损的映射关系得到第二个训练数据源，有效地补充和增强了数据之间的物理特性，解决了实际网络训练中标注数据不足的问题。随后，开发了一种集成串行卷积、并行卷积、双向门控循环单元 （BiGRU） 和注意力机制的结构，以提取时间序列数据中的空间和时间特征。此外，基于刀具磨损的物理定律，提出了一种具有物理约束的改进损失函数，以提高模型的物理一致性。实验结果表明，与单个数据源相比，增强数据后模型预测的 RMSE 误差降低了 12.67%，而改进损失函数后，预测结果的 RMSE 误差最多降低了 25.16%。该模型在较短的训练 epoch 内具有较高的预测精度和良好的实时性能。 所提出的方法提供了一种基于物理和数据信息融合的建模策略，具有较低的计算资源要求。