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

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...