In recent years, the manufacturing industry has faced various global challenges. One of these challenges is the increasing frequent adjustment and reconfiguration of production lines, necessitated by the diversification of customer demands. To improve the processing efficiency after production line reconfiguration, this paper puts forward a group learning architecture towards intelligent equipment. With this architecture, swarm intelligence can be accomplished through group learning. From the perspective of edge intelligence, this paper addresses key technology issues within the areas of data acquisition and preprocessing, cyber-physical fusion, knowledge extraction and sharing, and equipment performance self-optimization. The proposed approaches are much more useful for improving the processing efficiency of the reconfigured production line.
History
School
Aeronautical, Automotive, Chemical and Materials Engineering
This paper was accepted for publication in the journal Computers and Electrical Engineering and the definitive published version is available at https://doi.org/10.1016/j.compeleceng.2021.107119