Defect identification is a crucial task for process monitoring and quality evaluation in additive manufacturing (AM). Deep learning (DL) has shown great potential for diverse fields, but some challenges have hindered the application in AM process monitoring. Firstly, DL-based methods are driven by big data and require a large number of training data. However, in reality, defective data is often rare, so different AM manufacturers can collaborate to train a global model for detecting defects. Nevertheless, due to privacy concerns and conflicts of interest, data-sharing between different manufacturers might be dangerous. Additionally, the heterogeneities among manufacturers’ data leads to the domain shift, making it difficult to obtain a well-generalized model. Moreover, the imbalance issue of powder-spreading defects seriously damages the performance of the defect recognition. In this paper, we proposed FTLAM, a federated transfer learning method, to address the above issues. Concretely, to solve the data privacy concerns, federated learning (FL) is utilized to collaboratively train a global model between different AM manufacturers without exchanging original data. Furthermore, a client-oriented transfer method is proposed to mitigate the heterogeneities across multiple clients and improve the model's generalization. Meanwhile, a multi-loss joint optimization approach is designed to alleviate the data imbalance. Extensive experiments on laser powder bed fusion (LPBF) image datasets of powder-spreading have demonstrated that our FTLAM can obtain satisfactory performance of powder spreading defect identification.

中文翻译：

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保护隐私的联合迁移学习，用于从增材制造中高度不平衡的图像数据中识别缺陷

缺陷识别是增材制造 (AM) 过程监控和质量评估的一项关键任务。深度学习（DL）在不同领域展现出巨大潜力，但一些挑战阻碍了其在增材制造过程监控中的应用。首先，基于深度学习的方法由大数据驱动，需要大量的训练数据。然而，实际上，缺陷数据通常很少见，因此不同的增材制造制造商可以合作训练用于检测缺陷的全局模型。然而，由于隐私问题和利益冲突，不同制造商之间的数据共享可能存在危险。此外，制造商数据之间的异构性导致领域转移，使得很难获得通用的模型。此外，粉末铺展缺陷的不平衡问题严重损害了缺陷识别的性能。在本文中，我们提出了联邦迁移学习方法FTLAM来解决上述问题。具体来说，为了解决数据隐私问题，利用联邦学习（FL）在不同增材制造制造商之间协作训练全局模型，而无需交换原始数据。此外，提出了一种面向客户端的传输方法来减轻多个客户端之间的异构性并提高模型的泛化性。同时，设计了多损失联合优化方法来缓解数据不平衡。对粉末铺展激光粉末床融合（LPBF）图像数据集的大量实验表明，我们的 FTLAM 可以获得令人满意的粉末铺展缺陷识别性能。