Machining quality prediction of multi-feature parts has been a challenging problem because of small dataset and inconsistent quality data distribution with respect to each machining feature. Transfer learning that leverages knowledge of one task and can be repurposed on another task seems a good solution for this purpose. However, traditional transfer learning typically has a single source domain and a target domain, which limits its applications in multi-source scenarios (e.g., multi-feature). To solve this issue, this paper proposes a novel integrated multi-source domain dynamic adaptive transfer learning (IMD-DATL) framework for machining quality prediction of multi-feature part machining systems. Specifically, a domain-sample similarity double matching multi-source domain integration method is designed to construct the integration knowledge transfer from multiple source domains to the target domain. A residual feature extraction network based on sample entropy-dynamic channel double-layer attention structure and a fine-grained transferable feature attention module are designed. These three attentions are used to improve the feature learning ability and the adaptation level to the predicted object in the three dimensions of sample, channel and data feature. Finally, multiple sets of comparative experiments in thin-walled part machining systems confirm the effectiveness and superiority of the proposed method for cross-domain quality prediction. Compared with other traditional transfer learning methods, the MAE, RMSE and Score on average of this method are increased by 5.47 %, 4.59 % and 4.84 %, respectively, compared with other multi-source domain adaptation methods, the MAE, RMSE and Score on average of this method are increased by 7.13 %, 7.37 % and 6.52 %, respectively.

中文翻译：

---

利用集成多源域动态自适应迁移学习预测多特征零件的加工质量


由于数据集较小且每个加工特征的质量数据分布不一致，多特征零件的加工质量预测一直是一个具有挑战性的问题。利用一项任务的知识并可以重新用于另一项任务的迁移学习似乎是实现此目的的一个很好的解决方案。然而，传统的迁移学习通常具有单个源域和目标域，这限制了其在多源场景（例如多特征）中的应用。为了解决这个问题，本文提出了一种新颖的集成多源域动态自适应迁移学习（IMD-DATL）框架，用于多特征零件加工系统的加工质量预测。具体来说，设计了领域样本相似度双匹配多源领域集成方法来构建从多个源领域到目标领域的集成知识迁移。设计了基于样本熵-动态通道双层注意力结构的残差特征提取网络和细粒度可转移特征注意力模块。这三个注意力用于在样本、通道和数据特征三个维度上提高特征学习能力和对预测对象的适应水平。最后，在薄壁零件加工系统中进行的多组对比实验证实了所提出的跨域质量预测方法的有效性和优越性。与其他传统迁移学习方法相比，该方法的MAE、RMSE和Score平均分别比其他多源域适应方法提高了5.47%、4.59%和4.84%。该方法的平均值分别提高了 7.13%、7.37% 和 6。分别为 52%。