Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Oct 3;18(1):420.
doi: 10.1186/s13104-025-07486-8.

An accelerometer-based dataset for monitoring slag in steel manufacturing

Mert Sehri  1 , Lucas Mantuan Ayres  2 , Francisco de Assis Boldt  3 , Patrick Dumond  4 , Marco Antonio de Souza Leite Cuadros  5
Affiliations

An accelerometer-based dataset for monitoring slag in steel manufacturing

Mert Sehri et al. BMC Res Notes. .

Abstract

Objectives: Slag detection in steel manufacturing is essential for ensuring high product quality and process efficiency. The purpose of the accelerometer-based data is to allow for accurate monitoring and differentiation between slag and molten metal flow. This is vital to prevent equipment damage, maintain steel quality, and enhance operational effectiveness. The data is collected specifically to support the development of machine learning models for real-time monitoring in the steel production process, addressing the critical need for precise slag detection.

Data description: 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.

Keywords: Condition monitoring; Signal processing; Slag flow; Steel manufacturing; Vibration.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not Applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Accelerometer Data for E-5, B-6 and S-5
Fig. 2
Fig. 2
Data Collection Process
See this image and copyright information in PMC

References

    1. Guvenc MA, Kapusuz H, Mistikoglu S. Experimental study on accelerometer-based ladle slag detection in continuous casting process. Int J Adv Manuf Technol. 2020;106:2983–93. 10.1007/s00170-019-04830-8.
    1. Kapusuz H, Güvenç MA, Mıstıkoğlu S. A review study on ladle slag detection technologies in continuous cing process. Int Adv Res Eng J. n.d. https://dergipark.org.tr/en/pub/iarej/issue/50386/421657. Accessed 1 Oct 2024.
    1. Tsygankov Y, Kovriznich Y, Smirnov D, Poleshchenko D. Method to detect slag flow in course of steel casting based on machine learning algorithms. 4th Int Conf Control Syst Math Model Autom Energy Effi (SUMMA). 2022;2022:412–5. 10.1109/SUMMA57301.2022.9974154.
    1. Sehri M, Ayres LM, Boldt F, de Dumond A, de Cuadros P. SL. Steel Slag Flow Dataset 2024;1. 10.17632/mjnpsbs6fs.1.
    1. Shannon CE. A mathematical theory of communication. Bell Syst Tech J. 1948;27:379–423. 10.1002/j.1538-7305.1948.tb01338.x.

LinkOut - more resources