  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.
