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

arXiv: https://arxiv.org/abs/2105.14730 · HTML: https://mlsanity.com/paper/2105.14730/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 3 of 3)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing](https://mlsanity.com/paper/2101.00509/) (2021)
- [Transfer Learning as an Enabler of the Intelligent Digital Twin](https://mlsanity.com/paper/2012.01913/) (2020)

## Built on by (most cited first, 1 of 1)

- [Intelligent Exploration of Solution Spaces Exemplified by Industrial Reconfiguration Management](https://mlsanity.com/paper/2207.01693/) (2022)

## Abstract (arXiv)

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.