Artificial intelligence technique
SONG Jun, GAO Lei, WANG Kuiyue, CAO Zhonghua, MA Chiyu, MA Xiaoguo
Metallurgical Industry Automation.
Accepted: 2023-09-29
Traditional mechanical properties prediction and optimization methods are mostly based on experience and mechanisms, and do not fully consider the value contained in the data. One of the current research hot spots is how to explore the linear and nonlinear transfer relationship between steel performance and related process parameters, construct high-precision performance prediction models, and achieve process optimization. Based on the high-dimensional process quality dataset of the throughout manufacturing process of hot rolled strip, this paper proposes a performance optimization method for hot rolled strip steel that integrates machine learning performance prediction model and Shapley additive explanation (SHAP) interpretation framework. This method first uses MIC metrics to select effective variables that have a significant impact on mechanical performance indicators from high-dimensional process parameters dataset; Then, by comparing the prediction accuracy of performance prediction models based on MSVR, SVR, and random forest, the optimal performance prediction model is selected; Finally, based on the SHAP interpretation framework and optimal prediction model, process parameter evaluation is conducted to measure the quantitative impact of each process parameter on the final performance, and the operational variables are adjusted according to the results of SHAP analysis to verify the effectiveness of performance optimization. The experimental results indicate that the performance optimization method proposed in this paper can significantly improve performance indicators according to demand, and has guiding significance for mechanical performance control in steel production processes.
