An accelerometer-based dataset for monitoring slag in steel manufacturing
The Steel Slag Flow Dataset (SSFD) offers a comprehensive set of data obtained from a triaxial accelerometer during various stages of steel production. By leveraging this dataset, researchers can effectively analyze and classify the flow of slag versus molten metal. The dataset allows for data-driven approaches so that machine learning researchers can optimize steel manufacturing processes, ensuring high-quality steel production and minimizing the risks associated with slag contamination. The SSFD provides a valuable resource for researchers seeking to enhance predictive maintenance and monitoring in industrial applications.
Authors
- Patrick Dumond (ORCID: https://orcid.org/0000-0002-2200-4614)
- Mert Sehri (ORCID: https://orcid.org/0000-0003-4781-1719)
- Lucas Mantuan Ayres
- Francisco de Assis Boldt (ORCID: https://orcid.org/0000-0001-6919-5377)
- Marco Antonio de Souza Leite Cuadros (ORCID: https://orcid.org/0000-0003-4191-1794)
Institutions
- International Foundation for Electoral Systems (US)
- University of Ottawa (CA)
- Faculdade da Serra Gaúcha (BR)
- Instituto Federal do Espírito Santo (BR)
Publication Details
- Journal
- BMC Research Notes
- Published
- 2025-10-03
- DOI
- https://doi.org/10.1186/s13104-025-07486-8
- Citations
- 2
- Primary Topic
- Metallurgical Processes and Thermodynamics
- Type
- article
- Field-Weighted Citation Impact
- 1.43