Abstract
This study aims to predict the hardness of steel wire rods produced in the actual process by using statistical analysis and machine learning algorithms. The prediction model is built through five steps: operational data collection, key feature selection, experimental data collection, building a prediction model, and revising the model. We propose an innovative data collection methodology that mitigates the challenges posed by the wire rod’s form and resolves the issue of mismatch between different measurement locations. The effects of features on hardness of wire rod are estimated with correlation analysis and explainable AI method. To ensure the model’s robustness across varying process conditions, experimental data are collected via conducting a simulation test on key features that have a large effect. A hardness prediction model using experimental data is validated and revised using operational data. Several statistical indices ensure that the prediction model has good prediction performance. The proposed method has contribution in that it provides a systematic procedure to predict the hardness of wire rods using operational data from the wire rod rolling process. It serves as an effective tool for predicting and optimizing the hardness of steel wire rods.
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Funding
This work was supported by the Korea Evaluation Institute of Industrial Technology (KEIT) and the Ministry of Trade, Industry, & Energy (MOTIE) of the Republic of Korea (Grant number [RS-2022–00155473]: Development of energy efficiency improvement and quality improvement technology by applying big data in the steel rolling process).
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Conceptualization: Dong-Hee Lee. Methodology: Seok-Kyu Pyo, Dong-Hee Lee, Sung-Jun Hur. Formal analysis and investigation: Seok-Kyu Pyo, Sung-Jun Hur. Writing—original draft preparation: Seok-Kyu Pyo. Writing—review and editing: Dong-Hee Lee. Funding acquisition: Sang-Hyeon Lee, Sung-Jun Lim, Jong-Eun Lee, Hong-Kil Moon. Supervision: Dong-Hee Lee.
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Pyo, SK., Lee, DH., Hur, SJ. et al. Prediction of spring steel wire rod hardness based on wire rod rolling process data: a case study. Int J Adv Manuf Technol (2024). https://doi.org/10.1007/s00170-024-13740-3
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DOI: https://doi.org/10.1007/s00170-024-13740-3