Intelligent Quality Control and Defect Detection in Steel Manufacturing Processes Using Deep Learning and Image Processing for Energy Optimization

Document Type : Original Article

Authors

1 Assistant Professor, Department of Computer Engineering, Science and Arts University, Yazd, Iran

2 Teacher, Hormozgan Education, Rudan Education Computer Group, Hormozgan, Iran

10.22034/abmir.2025.23661.1167

Abstract

In recent years, the increasing competition in the steel industry and the urgent demand for reducing costs while improving quality have accelerated the adoption of artificial intelligence technologies. One of the major challenges in this domain is ensuring accurate product quality control and efficient energy management, both of which directly affect productivity and sustainability. This study proposes an integrated deep learning framework for simultaneous quality inspection and energy optimization in steel manufacturing. High-resolution surface images from the NEU surface defect dataset, combined with simulated energy consumption records, were pre-processed through filtering, normalization, and temporal alignment techniques. A hybrid multi-branch architecture, combining convolutional neural networks (CNN) for defect detection and CNN-LSTM for energy prediction, was implemented and optimized using the NSGA-II multi-objective algorithm. Experimental results demonstrate that the proposed approach achieved 98.7% accuracy and an F1-score of 98.5% in defect detection, while reducing the prediction error of energy consumption to an RMSE of 0.082 and MAPE of 2.1%. These improvements not only outperform recent state-of-the-art methods but also contribute to reducing rework, minimizing scrap rates, and achieving measurable energy savings. The proposed methodology offers a practical step toward green steel production and the realization of Industry 4.0 in heavy industries.

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Main Subjects

References

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Applied and basic Machine intelligence research
Volume 3, Issue 2
December 2025
Pages 141-158

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