Abstract
Smart manufacturing systems adapt to changes in the environment, which are detected via sensors. Decisions are then made by the control software of such manufacturing systems, e.g., by integrated AI-based algorithms. The design and evaluation of such algorithms require the availability of high-quality datasets. This paper provides an overview of the existing publicly available manufacturing datasets, offering a detailed exploration of the current landscape of shared data resources in the manufacturing sector and highlighting the utility of these datasets. The review identifies nine notable datasets with extensive documentation and comprehensive data coverage. Each of these datasets is described in detail and can be used for developing intelligent manufacturing systems, assessing their quality, and reporting on open gaps.
| Original language | English |
|---|---|
| Title of host publication | 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation, ETFA 2024 |
| Editors | Tullio Facchinetti, Angelo Cenedese, Lucia Lo Bello, Stefano Vitturi, Thilo Sauter, Federico Tramarin |
| Place of Publication | New York, USA |
| Publisher | IEEE |
| Pages | 1-8 |
| Number of pages | 8 |
| ISBN (Electronic) | 9798350361230 |
| ISBN (Print) | 979-8-3503-6123-0 |
| DOIs | |
| Publication status | Published - Oct 2024 |
Publication series
| Name | IEEE International Conference on Emerging Technologies and Factory Automation, ETFA |
|---|---|
| ISSN (Print) | 1946-0740 |
| ISSN (Electronic) | 1946-0759 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Fields of science
- 102022 Software development
- 102025 Distributed systems
- 102029 Practical computer science
- 202003 Automation
- 202017 Embedded systems
- 202041 Computer engineering
- 102 Computer Sciences
JKU Focus areas
- Digital Transformation
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