Hot deformation conditions have important influence on the final properties of 5CrNiMoV steel. Based on the developed high-throughput forging equipment, a combined method of high-throughput simulation and machine learning was put forward to efficiently explore the best deformation conditions for 5CrNiMoV steel. A dataset containing 960 sets of data was established, describing the average grain size, damage, and dynamic recrystallization volume fraction of samples, strain rates and temperatures. The RFR (Random Forest Regression) model was trained and used to predict the optimal hot deformation conditions of 5CrNiMoV steel. Based on the searching space and the screening strategies, the optimal hot deformation conditions of 5CrNiMoV at different strains was successfully achieved. The results show that the designed strategy could be used to improve the research efficiency for better production processes and provide a certain theoretical reference for further experimental verification.

热变形条件对5CrNiMoV钢的最终性能有重要影响。基于研制的高通量锻造设备，提出了一种高通量模拟与机器学习相结合的方法，以有效探索5CrNiMoV钢的最佳变形条件。建立了一个包含 960 组数据的数据集，描述了样品的平均晶粒尺寸、损伤和动态再结晶体积分数、应变速率和温度。训练 RFR（随机森林回归）模型并用于预测 5CrNiMoV 钢的最佳热变形条件。基于搜索空间和筛选策略，成功实现了5CrNiMoV在不同应变下的最佳热变形条件。