Privacy-preserving federated transfer learning for defect identification from highly imbalanced image data in additive manufacturing

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.