# A deep learning-enhanced Digital Twin framework for improving safety and reliability in human–robot collaborative manufacturing

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-deep-learning-enhanced-digital-twin-framework-for-improving-safety-and/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Shenglin Wang,Jingqiong Zhang,Peng Wang,James Law,Radu Călinescu,Lyudmila Mihaylova |
| Citations | 153 |
| DOI | 10.1016/j.rcim.2023.102608 |
| Fields | Engineering |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://doi.org/10.1016/j.rcim.2023.102608 |
| OpenAlex ID | https://openalex.org/W4384472141 |
| Type | article |
| Year | 2023 |

## Paper authors

- [Peng Wang](https://scholariq.org/researchers/peng-wang/)

## Paper journal

- [Robotics and Computer-Integrated Manufacturing](https://scholariq.org/journals/robotics-and-computer-integrated-manufacturing/)

## Paper primary topic

- [Digital Transformation in Industry](https://scholariq.org/topics/digital-transformation-in-industry/)

## Paper topics

- [Digital Transformation in Industry](https://scholariq.org/topics/digital-transformation-in-industry/)
- [Robot Manipulation and Learning](https://scholariq.org/topics/robot-manipulation-and-learning/)
- [Manufacturing Process and Optimization](https://scholariq.org/topics/manufacturing-process-and-optimization/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.