Transfer Learning as an Enhancement for Reconfiguration Management of Cyber-Physical Production Systems
This addresses reconfiguration management for manufacturing systems with ML components, but appears incremental as it builds on existing reconfiguration approaches.
The paper tackles the challenge of reconfiguring cyber-physical production systems when machine learning components are involved, by proposing the use of transfer learning to assess and recommission configurations with reduced effort, demonstrated on a real manufacturing system.
Reconfiguration demand is increasing due to frequent requirement changes for manufacturing systems. Recent approaches aim at investigating feasible configuration alternatives from which they select the optimal one. This relies on processes whose behavior is not reliant on e.g. the production sequence. However, when machine learning is used, components' behavior depends on the process' specifics, requiring additional concepts to successfully conduct reconfiguration management. Therefore, we propose the enhancement of the comprehensive reconfiguration management with transfer learning. This provides the ability to assess the machine learning dependent behavior of the different CPPS configurations with reduced effort and further assists the recommissioning of the chosen one. A real cyber-physical production system from the discrete manufacturing domain is utilized to demonstrate the aforementioned proposal.