Cross-conditions tool wear monitoring has a wide application prospect in manufacturing. However, the data distribution discrepancies caused by the inconsistency of process elements restrict the generalization of models under cross-conditions or even similar conditions. The existing methods based on diversity enhancement make it difficult to effectively establish the correlation between the source domain and target domains, which limits the improvement of model generalization. Therefore, this paper details the cause of data distribution discrepancies and proposes a discontinuous physical property-constrained single-source domain generalization network for milling tool wear monitoring. Firstly, a spatial attention mechanism is introduced to weight key signal segments adaptively. Secondly, the generation module is constrained by a standard sample and is used to generate diverse samples with physical properties. Thirdly, extensive recognition experiments on an open dataset and machining experiments with three distribution discrepancy levels were conducted to verify the effectiveness of the proposed method. Finally, feature visualizations provide consistency and interpretability.

中文翻译：

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DP2Net：一种用于刀具磨损状态识别的不连续物理属性约束的单源域泛化网络

跨条件刀具磨损监测在制造业中具有广泛的应用前景。然而，过程要素的不一致导致的数据分布差异限制了模型在交叉条件甚至相似条件下的泛化。现有基于多样性增强的方法难以有效建立源域和目标域之间的相关性，限制了模型泛化能力的提高。因此，本文详细介绍了数据分布差异的原因，并提出了一种用于铣削刀具磨损监测的不连续物理属性约束的单源域泛化网络。首先，引入空间注意机制对关键信号片段进行自适应加权。其次，生成模块以标准样本为约束，用于生成具有物理性质的多样化样本。第三，在开放数据集上进行了广泛的识别实验和三个分布差异级别的加工实验，以验证所提方法的有效性。最后，特征可视化提供一致性和可解释性。