Distributed data filtering and modeling for fog and networked manufacturing

Abstract

Fog Manufacturing applies both Fog and Cloud Computing collaboratively in Smart Manufacturing to create an interconnected network through sensing, actuation, and computation nodes. Fog Manufacturing has become a promising research component to be integrated into the existing Smart Manufacturing paradigm and provides reliable and responsive computation services. However, Fog nodes' relatively limited communication bandwidth and computation capabilities call for reduced data communication load and computation time latency for modeling. There has long been a lack of an integrated framework to automatically reduce manufacturing data and perform computationally efficient modeling/machine learning. This research direction is increasingly important as both the computational demands and Fog/networked Manufacturing become prevalent. This paper proposes an integrated and distributed framework for data reduction and modeling of multiple systems in a Smart Manufacturing network considering the system similarities. A simulation study and a Fog Manufacturing testbed for ingot growth manufacturing validated that the proposed framework significantly reduces the sample size used for improved computational runtime metrics while outperforming various other data reduction methods in modeling performance.