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<p>The transition from primary to secondary aluminium production offers substantial environmental benefits but introduces challenges related to impurity-induced changes in microstructure and final materials performance in mechanical integrity and corrosion resistance. This study investigates the influence of most common Fe, Mn, and Cu impurities on the formation of intermetallic phases in AlSi7Mg0.3 alloys using a combined CALPHAD and machine learning framework. High-throughput Scheil solidification simulations were conducted on 4999 alloy compositions using Thermo-Calc, and the resulting data were used to train and validate a Random Forest regression model. The model exhibited robust predictive performance (R<sup>2</sup> = 0.98, NRMSE = 0.07, NMAE = 0.05) and was employed to compute phase fractions for over 20 million alloy compositions within the defined impurity space. SHAP-based feature analysis revealed interactions, direct as well as indirect, between impurity elements and key phases, highlighting the opposing roles of Fe and Mn in stabilizing <i>β</i>-Al<sub>5</sub>FeSi (AL9FE2SI2) and Al15SI2M4. And the resulting impurity-phase maps provide quantitative, thermodynamics-based decision support for impurity management and manganese optimization in recycled aluminium alloys.</p>

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Machine learning-accelerated CALPHAD analysis of impurity-driven intermetallic formation in secondary AlSi7Mg0.3

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月16日

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  • Lukas C. Jarren,
  • Alexandre Viardin,
  • Eugen Gazenbiller,
  • Markus Apel,
  • Janin Eiken,
  • Qiqi Li,
  • Mikhail Zheludkevich,
  • Daniel Höche

摘要

The transition from primary to secondary aluminium production offers substantial environmental benefits but introduces challenges related to impurity-induced changes in microstructure and final materials performance in mechanical integrity and corrosion resistance. This study investigates the influence of most common Fe, Mn, and Cu impurities on the formation of intermetallic phases in AlSi7Mg0.3 alloys using a combined CALPHAD and machine learning framework. High-throughput Scheil solidification simulations were conducted on 4999 alloy compositions using Thermo-Calc, and the resulting data were used to train and validate a Random Forest regression model. The model exhibited robust predictive performance (R2 = 0.98, NRMSE = 0.07, NMAE = 0.05) and was employed to compute phase fractions for over 20 million alloy compositions within the defined impurity space. SHAP-based feature analysis revealed interactions, direct as well as indirect, between impurity elements and key phases, highlighting the opposing roles of Fe and Mn in stabilizing β-Al5FeSi (AL9FE2SI2) and Al15SI2M4. And the resulting impurity-phase maps provide quantitative, thermodynamics-based decision support for impurity management and manganese optimization in recycled aluminium alloys.