Optimization of Process Parameters in Electron Beam Cold Hearth Melting and Casting of Ti-6wt%Al-4wt%V via CFD-ML Approach Article Swipe
Yuchen Xin
,
Jianglu Liu
,
Yaming Shi
,
Z. Cheng
,
Yang Liu
,
Lei Gao
,
Huanhuan Zhang
,
Haohang Ji
,
Tong Han
,
Shenghui Guo
,
Shubiao Yin
,
Qiuni Zhao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/met15080897
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/met15080897
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/met15080897
- OA Status
- gold
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413215854
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Raw OpenAlex JSON
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https://openalex.org/W4413215854Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/met15080897Digital Object Identifier
- Title
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Optimization of Process Parameters in Electron Beam Cold Hearth Melting and Casting of Ti-6wt%Al-4wt%V via CFD-ML ApproachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-11Full publication date if available
- Authors
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Yuchen Xin, Jianglu Liu, Yaming Shi, Z. Cheng, Yang Liu, Lei Gao, Huanhuan Zhang, Haohang Ji, Tong Han, Shenghui Guo, Shubiao Yin, Qiuni ZhaoList of authors in order
- Landing page
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https://doi.org/10.3390/met15080897Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/met15080897Direct OA link when available
- Concepts
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Computational fluid dynamics, Ingot, Materials science, Multiphysics, Mechanics, Diffusion, Mechanical engineering, Metallurgy, Thermodynamics, Structural engineering, Engineering, Alloy, Finite element method, PhysicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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