In advanced manufacturing, the incorporation of sensing technology provides an opportunity to achieve efficient in-situ process monitoring using machine learning methods. Meanwhile, the advances of information technologies also enable a connected and decentralized environment for manufacturing systems, making different manufacturing units in the system collaborate more closely. In a decentralized manufacturing system, the involved units may fabricate same or similar products and deploy their own machine learning model for online process monitoring. However, due to the possible inconsistency of task progress during the operation, it is also common that some units have more informative data while some have less informative data. Thus, the monitoring performance of machine learning model for each unit may highly vary. Therefore, it is extremely valuable to achieve efficient and secured knowledge sharing among the units in a decentralized manufacturing system for enhancement of poorly performed models. To realize this goal, this paper proposes a novel knowledge distillation-based information sharing (KD-IS) framework, which could distill informative knowledge from well performed models to improve the monitoring performance of poorly performed models. To validate the effectiveness of this method, a real-world case study is conducted in a connected fused filament fabrication (FFF)-based additive manufacturing (AM) platform. The experimental results show that the developed method is very efficient in improving model monitoring performance at poorly performed models, with solid protection on potential data privacy.

翻译：在先进制造中，传感技术的引入为利用机器学习方法实现高效原位过程监控提供了契机。与此同时，信息技术的进步也使制造系统能够构建互联互通的分散化环境，促使系统中不同制造单元实现更紧密协作。在分散制造系统中，各关联单元可能生产相同或相似产品，并部署各自机器学习模型进行在线过程监控。然而，由于运行过程中任务进度的不一致性，常出现部分单元拥有更丰富信息数据而其他单元数据信息量较少的情况。这导致各单元机器学习模型的监控性能可能存在显著差异。因此，在分散制造系统中实现单元间高效、安全的知识共享以提升性能较差的模型具有极高的应用价值。为实现该目标，本文提出了一种基于知识蒸馏的信息共享框架（KD-IS），该框架能够从性能优异的模型中提炼有效知识，进而提升性能欠佳模型的监控能力。为验证方法的有效性，在基于熔融沉积成型（FFF）的增材制造（AM）互联平台上开展了真实案例研究。实验结果表明，该方法在保护数据隐私的前提下，能显著提升性能欠佳模型的过程监控性能。