Optimization of Process Parameters in Electron Beam Cold Hearth Melting and Casting of Ti-6wt%Al-4wt%V via CFD-ML Approach

• Published in: Metals (Basel ) Vol. 15; no. 8; p. 897
• Main Authors: Xin, Yuchen, Liu, Jianglu, Shi, Yaming, Cheng, Zina, Liu, Yang, Gao, Lei, Zhang, Huanhuan, Ji, Haohang, Han, Tianrui, Guo, Shenghui, Yin, Shubiao, Zhao, Qiuni
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 11.08.2025
• Subjects: Aircraft; Algorithms; Aluminum; Aluminum alloys; Artificial intelligence; Back propagation; Back propagation networks; Cold; Computational fluid dynamics; Computer simulation; Computing costs; Continuous casting; Corrosion resistance; Diffusion; Electron beams; Energy consumption; Ingot casting; Ingots; Machine learning; Magnesium alloys; Manufacturing; Mechanical properties; Melt pools; Neural networks; Optimization; Prediction models; Process parameters; Simulation; Solidification; Temperature distribution; Titanium alloys; Titanium base alloys; Vanadium; Vaporization
• DOI: 10.3390/met15080897
• ISSN: 2075-4701

During electron beam cold hearth melting (EBCHM) of Ti-6wt%Al-4wt%V titanium alloy, aluminum volatilization causes compositional segregation in the ingot, significantly degrading material performance. Traditional methods (e.g., the Langmuir equation) struggle to accurately predict aluminum diffusion and compensation behaviors, while computational fluid dynamics (CFD), although capable of resolving multiphysics fields in the molten pool, suffer from high computational costs and insufficient research on segregation control. To address these issues, this study proposes a CFD-machine learning (backpropagation neural network, CFD-ML(BP)) approach to achieve precise prediction and optimization of aluminum segregation. First, CFD simulations are performed to obtain the molten pool’s temperature field, flow field, and aluminum concentration distribution, with model reliability validated experimentally. Subsequently, a BP neural network is trained using large-scale CFD datasets to establish an aluminum concentration prediction model, capturing the nonlinear relationships between process parameters (e.g., casting speed, temperature) and compositional segregation. Finally, optimization algorithms are applied to determine optimal process parameters, which are validated via CFD multiphysics coupling simulations. The results demonstrate that this method predicts the average aluminum concentration in the ingot with an error of ≤3%, significantly reducing computational costs. It also elucidates the kinetic mechanisms of aluminum volatilization and diffusion, revealing that non-monotonic segregation trends arise from the dynamic balance of volatilization, diffusion, convection, and solidification. Moreover, the most uniform aluminum distribution (average 6.8 wt.%, R2 = 0.002) is achieved in a double-overflow mold at a casting speed of 18 mm/min and a temperature of 2168 K.