Transfer Learning as an Enhancement for Reconfiguration Management of Cyber-Physical Production Systems

TL;DR

This paper proposes enhancing reconfiguration management in cyber-physical production systems using transfer learning to efficiently evaluate configuration behaviors and support recommissioning, demonstrated on a real manufacturing system.

Contribution

It introduces a transfer learning approach to improve reconfiguration management in CPPS, reducing effort and aiding recommissioning of configurations.

Findings

Transfer learning effectively assesses configuration behaviors.

Reduces effort in reconfiguration decision-making.

Supports efficient recommissioning of CPPS configurations.

Abstract

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.