An Industrial Composition Prediction Methodology Based on a Stage-Dependent Thermodynamic–Kinetic Prediction Framework for Aluminum Volatilization During Electron Beam Cold Hearth Melting of TA18 Titanium Alloy

Aluminum volatilization during electron beam cold hearth melting (EBCHM) makes precise composition control of titanium alloys difficult. In this study, a thermodynamic–kinetic framework was established to describe Al evaporation, and a Global Prediction Model (GPM) and Stage-Dependent Prediction Model (SPM) were developed for composition prediction and process analysis. Under the reported comparison basis, the GPM predicted final Al content with a mean absolute percentage error (MAPE) of 4.51% and a maximum error of 8.47%; a first-order normalization of the 4.5 wt.% theoretical predictions to the 4.4 wt.% industrial charging basis reduced the concentration MAPE to approximately 2.10%, showing that part of the original offset arose from the charge-basis mismatch. The SPM showed a MAPE of 8.33% for final Al concentration. Both models systematically underestimated the measured Al loss, indicating a directional bias that remains to be resolved. The SPM predicted that the refining and melting hearths contributed approximately 44.4% and 32.5%, respectively, or 76.9% in total; these percentages are model-derived stage attributions and were not independently validated by stage-resolved measurements. The GPM is therefore most suitable for rapid overall composition prediction, whereas the SPM provides physically interpretable, stage-specific guidance for identifying candidate critical volatilization regions and optimizing EBCHM operating conditions.

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Journal
Metals
Published
2026-09-21
DOI
https://doi.org/10.3390/met16091049
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
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article

An Industrial Composition Prediction Methodology Based on a Stage-Dependent Thermodynamic–Kinetic Prediction Framework for Aluminum Volatilization During Electron Beam Cold Hearth Melting of TA18 Titanium Alloy

裴腾, Bingyao Yan, Zhe Wang, Yang Li et al.
Metals
Additive Manufacturing Materials and Processes
article

An Industrial Composition Prediction Methodology Based on a Stage-Dependent Thermodynamic–Kinetic Prediction Framework for Aluminum Volatilization During Electron Beam Cold Hearth Melting of TA18 Titanium Alloy

裴腾, Bingyao Yan, Zhe Wang, Yang Li, Peng Jiang, Jinshan Li, Jiangkun Fan, Zhenghong Liu, Huifa Tao, Bobo Li, Xiaobo Hao, Peng Lin
article en

Abstract

Aluminum volatilization during electron beam cold hearth melting (EBCHM) makes precise composition control of titanium alloys difficult. In this study, a thermodynamic–kinetic framework was established to describe Al evaporation, and a Global Prediction Model (GPM) and Stage-Dependent Prediction Model (SPM) were developed for composition prediction and process analysis. Under the reported comparison basis, the GPM predicted final Al content with a mean absolute percentage error (MAPE) of 4.51% and a maximum error of 8.47%; a first-order normalization of the 4.5 wt.% theoretical predictions to the 4.4 wt.% industrial charging basis reduced the concentration MAPE to approximately 2.10%, showing that part of the original offset arose from the charge-basis mismatch. The SPM showed a MAPE of 8.33% for final Al concentration. Both models systematically underestimated the measured Al loss, indicating a directional bias that remains to be resolved. The SPM predicted that the refining and melting hearths contributed approximately 44.4% and 32.5%, respectively, or 76.9% in total; these percentages are model-derived stage attributions and were not independently validated by stage-resolved measurements. The GPM is therefore most suitable for rapid overall composition prediction, whereas the SPM provides physically interpretable, stage-specific guidance for identifying candidate critical volatilization regions and optimizing EBCHM operating conditions.

MetalsVol. 16(9)
Northwestern Polytechnical University (CN), Luoyang Institute of Science and Technology (CN), Taiyuan University of Technology (CN)
Openalex Percentile: Top 20%
Additive Manufacturing Materials and Processes
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