Uncertainty-Aware Machine-Learning Framework for Predicting Dislocation Plasticity and Stress-Strain Response in FCC Alloys

• URL: http://arxiv.org/abs/2506.20839v1
• Date: Wed, 25 Jun 2025 21:18:14 GMT
• Title: Uncertainty-Aware Machine-Learning Framework for Predicting Dislocation Plasticity and Stress-Strain Response in FCC Alloys
• Authors: Jing Luo, Yejun Gu, Yanfei Wang, Xiaolong Ma, Jaafar. A El-Awady,
• Abstract summary: Machine learning has significantly advanced the understanding and application of structural materials.<n>This study presents a comprehensive methodology utilizing a mixed density network (MDN) model.<n>The incorporation of statistical parameters of those predicted distributions into a dislocation-mediated plasticity model allows for accurate stress-strain predictions.
• Score: 9.066691897904875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning has significantly advanced the understanding and application of structural materials, with an increasing emphasis on integrating existing data and quantifying uncertainties in predictive modeling. This study presents a comprehensive methodology utilizing a mixed density network (MDN) model, trained on extensive experimental data from literature. This approach uniquely predicts the distribution of dislocation density, inferred as a latent variable, and the resulting stress distribution at the grain level. The incorporation of statistical parameters of those predicted distributions into a dislocation-mediated plasticity model allows for accurate stress-strain predictions with explicit uncertainty quantification. This strategy not only improves the accuracy and reliability of mechanical property predictions but also plays a vital role in optimizing alloy design, thereby facilitating the development of new materials in a rapidly evolving industry.

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