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<p>The model-based correlation between the chemical composition, process parameters, and mechanical property of the steel lies at the heart of the design of rolling process optimization. Yet, the hot rolling process is characterized by tightly coupling, many variables, and nonlinearity. The complicated link between the chemical composition, process factors, and mechanical properties of the high strength steel makes it difficult to construct a mathematical equation. On the basis of industry data for hot rolling, thermodynamic methods were applied to compute the effective Ti concentration here. Random forest was used to create the corresponding relationship model of the chemical composition, process parameters, and mechanical property for the high strength steel, obtaining a high level of mechanical property prediction precision. The root mean squared error for predicting yield strength is 21.07&#xa0;MPa, for predicting tensile strength it is 19.12&#xa0;MPa, and for predicting elongation it is 2.18%. Using the same chemical composition billet in conjunction with multi-objective evolutionary algorithm based on decomposition algorithm algorithm and taking into account the limits of the process circumstances, the best designs for the hot rolling process of different strength level steels are accomplished. The viability of process optimization is determined by industrial tests and theoretical analysis of the strength increment.</p>

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Machine learning assisted adaptable rolling for high strength steel

  • 期刊论文

  • 发表日期：

    2026年5月13日

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  • Guang-Ming Cao,
  • Yu-Ting He,
  • Hong-Bing Wang,
  • Si-Wei Wu,
  • Qi-Ming Jiang,
  • Cheng-De Zhang,
  • Zhi-Wei Gao,
  • Ning Liu,
  • Zhen-Yu Liu

摘要

The model-based correlation between the chemical composition, process parameters, and mechanical property of the steel lies at the heart of the design of rolling process optimization. Yet, the hot rolling process is characterized by tightly coupling, many variables, and nonlinearity. The complicated link between the chemical composition, process factors, and mechanical properties of the high strength steel makes it difficult to construct a mathematical equation. On the basis of industry data for hot rolling, thermodynamic methods were applied to compute the effective Ti concentration here. Random forest was used to create the corresponding relationship model of the chemical composition, process parameters, and mechanical property for the high strength steel, obtaining a high level of mechanical property prediction precision. The root mean squared error for predicting yield strength is 21.07 MPa, for predicting tensile strength it is 19.12 MPa, and for predicting elongation it is 2.18%. Using the same chemical composition billet in conjunction with multi-objective evolutionary algorithm based on decomposition algorithm algorithm and taking into account the limits of the process circumstances, the best designs for the hot rolling process of different strength level steels are accomplished. The viability of process optimization is determined by industrial tests and theoretical analysis of the strength increment.