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This paper aims to predict the reliability and quality factors in the steel manufacturing industry, such as temperature anddecarbonization thickness, by using artificial intelligence to improve productivity and quality. The data obtained from a special steel smallrolling process were learned, and the major control and quality factors were predicted using machine learning. Machine-learning-basedprediction data were compared with actual measurement data. Then, the differences in data values were evaluated. The predicted datavalues are closer than the actual data.
