Physical-informed machine learning of phase transformation and application to cooling path optimization

The flexible optimization control of the cooling path after hot rolling of strip steel has a significant influence on the phase transformation microstructure, which in turn determines the stability of mechanical properties and final product quality. To further analyze the complex coupling relationship between cooling path parameters and mechanical properties, a physically guided multi-objective optimization strategy is proposed for post-rolling cooling path control, combining continuous cooling transformation (CCT) diagram to achieve precise regulation of phase transformation microstructure and volume fraction. First, based on experimental data and machine learning algorithms, predictive models for the relationships among chemical composition, physical metallurgical parameters, and phase transformation temperatures are established, enabling accurate prediction of the CCT diagram. Subsequently, multi-objective optimization is applied to determine optimal cooling paths, with results compared against actual industrial production data. Finally, the optimization outcomes are validated through metallographic analysis and mechanical property testing, while the grain refinement strengthening theory is employed to analyze how CCT diagram-based cooling path optimization affects material properties. This approach achieves the goal of enhancing mechanical properties in hot rolled structural steels through controlled cooling path processes.