# Graph Learning for Cognitive Digital Twins in Manufacturing Systems

arXiv: https://arxiv.org/abs/2109.08632 · HTML: https://mlsanity.com/paper/2109.08632/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 48 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning](https://mlsanity.com/paper/2106.01086/) (2021)
- [Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation](https://mlsanity.com/paper/2006.16556/) (2020)
- [Future Automation Engineering using Structural Graph Convolutional Neural Networks](https://mlsanity.com/paper/1808.08213/) (2018)

## Built on by (most cited first, 2 of 2)

- [Metarobotics for Industry and Society: Vision, Technologies, and Opportunities](https://mlsanity.com/paper/2404.00797/) (2024)
- [Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry](https://mlsanity.com/paper/2407.02275/) (2024)

## Abstract (arXiv)

Future manufacturing requires complex systems that connect simulation platforms and virtualization with physical data from industrial processes. Digital twins incorporate a physical twin, a digital twin, and the connection between the two. Benefits of using digital twins, especially in manufacturing, are abundant as they can increase efficiency across an entire manufacturing life-cycle. The digital twin concept has become increasingly sophisticated and capable over time, enabled by rises in many technologies. In this paper, we detail the cognitive digital twin as the next stage of advancement of a digital twin that will help realize the vision of Industry 4.0. Cognitive digital twins will allow enterprises to creatively, effectively, and efficiently exploit implicit knowledge drawn from the experience of existing manufacturing systems. They also enable more autonomous decisions and control, while improving the performance across the enterprise (at scale). This paper presents graph learning as one potential pathway towards enabling cognitive functionalities in manufacturing digital twins. A novel approach to realize cognitive digital twins in the product design stage of manufacturing that utilizes graph learning is presented.