中国有色金属学报(英文版)
Transactions of Nonferrous Metals Society of China
| Vol. 36 No. 1 January 2026 |
（a Key Laboratory for Advanced Materials Processing (MOE), Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China;
b Beijing Laboratory of Metallic Materials and Processing for Modern Transportation, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China;
c Institute of Materials Genome Engineering, Henan Academy of Sciences, Zhengzhou 450046, China;
d School of Materials Science and Engineering, Central South University, Changsha 410083, China;
e State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, China;
f Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China）
Abstract:Machine learning-assisted methods for rapid and accurate prediction of temperature field, mushy zone, and grain size were proposed for the heating-cooling combined mold (HCCM) horizontal continuous casting of C70250 alloy plates. First, finite element simulations of casting processes were carried out with various parameters to build a dataset. Subsequently, different machine learning algorithms were employed to achieve high precision in predicting temperature fields, mushy zone locations, mushy zone inclination angle, and billet grain size. Finally, the process parameters were quickly optimized using a strategy consisting of random generation, prediction, and screening, allowing the mushy zone to be controlled to the desired target. The optimized parameters are 1234 °C for heating mold temperature, 47 mm/min for casting speed, and 10 L/min for cooling water flow rate. The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.
Key words: Cu alloy; numerical simulation; machine learning; prediction model; process optimization
