# Enhancing an Intelligent Digital Twin with a Self-organized Reconfiguration Management based on Adaptive Process Models

arXiv: https://arxiv.org/abs/2107.03324 · HTML: https://mlsanity.com/paper/2107.03324/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning](https://mlsanity.com/paper/2012.03301/) (2020)
- [Transfer Learning as an Enabler of the Intelligent Digital Twin](https://mlsanity.com/paper/2012.01913/) (2020)

## Built on by (most cited first, 3 of 3)

- [Towards autonomous system: flexible modular production system enhanced with large language model agents](https://mlsanity.com/paper/2304.14721/) (2023)
- [Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection](https://mlsanity.com/paper/2204.01620/) (2022)
- [Intelligent Exploration of Solution Spaces Exemplified by Industrial Reconfiguration Management](https://mlsanity.com/paper/2207.01693/) (2022)

## Abstract (arXiv)

Shorter product life cycles and increasing individualization of production leads to an increased reconfiguration demand in the domain of industrial automation systems, which will be dominated by cyber-physical production systems in the future. In constantly changing systems, however, not all configuration alternatives of the almost infinite state space are fully understood. Thus, certain configurations can lead to process instability, a reduction in quality or machine failures. Therefore, this paper presents an approach that enhances an intelligent Digital Twin with a self-organized reconfiguration management based on adaptive process models in order to find optimized configurations more comprehensively.