Journal of Powder Materials

Research Article

[Korean]
Development of Aluminum Alloys for Additive Manufacturing Using Machine Learning: Sungbin An, Juyeon Han, Seoyeon Jeon, Dowon Kim, Jae Bok Seol, Hyunjoo Choi; J Powder Mater. 2025;32(3):202-211. Published online June 30, 2025; DOI: https://doi.org/10.4150/jpm.2025.00150

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The present study introduces a machine learning approach for designing new aluminum alloys tailored for directed energy deposition additive manufacturing, achieving an optimal balance between hardness and conductivity. Utilizing a comprehensive database of powder compositions, process parameters, and material properties, predictive models—including an artificial neural network and a gradient boosting regression model, were developed. Additionally, a variational autoencoder was employed to model input data distributions and generate novel process data for aluminum-based powders. The similarity between the generated data and the experimental data was evaluated using K-nearest neighbor classification and t-distributed stochastic neighbor embedding, with accuracy and the F1-score as metrics. The results demonstrated a close alignment, with nearly 90% accuracy, in numerical metrics and data distribution patterns. This work highlights the potential of machine learning to extend beyond multi-property prediction, enabling the generation of innovative process data for material design.

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Predictive Control of Magnesium Content in Industrial 5182 Aluminum Alloy Recycling Using SCADA-Guided Gradient Boosting
Mengya Wang, Jiahui Xu, Xiaohu Wang, Farid Wirawan, Mouhamadou Aziz Diop
Journal of Materials Engineering and Performance.2026;[Epub] CrossRef

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