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<p>This study investigates the mechanisms of precipitate strengthening by employing discrete dislocation dynamics (DDD) simulations to model dislocation shearing and bypassing processes, and to quantify the critical resolved shear stress (CRSS) for dislocations to overcome precipitates. The simulations reveal that, when shearing is the dominant mechanism, the CRSS increases with precipitate size, whereas under bypass-dominated conditions, it decreases as precipitate size increases. To interpret and quantitatively predict the DDD simulation outcomes, modified theoretical models are proposed. To address the limitations of the theoretical models, machine learning (ML) techniques are further employed. The ML analysis identifies a strong positive correlation between precipitate volume fraction and CRSS. Precipitate size exhibits a negative correlation with CRSS in bypass-dominated scenarios, but a positive correlation under shearing-dominated conditions. The influence of dislocation type is found to be comparatively minor. By integrating DDD simulations, theoretical modeling, and ML analysis, this study provides a comprehensive understanding of precipitate strengthening mechanisms.</p>

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The Dislocation Dynamics Study and Machine Learning Approach of the Critical Stress for Dislocation Shearing and Bypassing Precipitates

  • 期刊论文

  • 发表日期：

    2025年10月31日

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  • Junshi Yu,
  • Jin Tao,
  • Fuping Yuan,
  • Xu Zhang

摘要

This study investigates the mechanisms of precipitate strengthening by employing discrete dislocation dynamics (DDD) simulations to model dislocation shearing and bypassing processes, and to quantify the critical resolved shear stress (CRSS) for dislocations to overcome precipitates. The simulations reveal that, when shearing is the dominant mechanism, the CRSS increases with precipitate size, whereas under bypass-dominated conditions, it decreases as precipitate size increases. To interpret and quantitatively predict the DDD simulation outcomes, modified theoretical models are proposed. To address the limitations of the theoretical models, machine learning (ML) techniques are further employed. The ML analysis identifies a strong positive correlation between precipitate volume fraction and CRSS. Precipitate size exhibits a negative correlation with CRSS in bypass-dominated scenarios, but a positive correlation under shearing-dominated conditions. The influence of dislocation type is found to be comparatively minor. By integrating DDD simulations, theoretical modeling, and ML analysis, this study provides a comprehensive understanding of precipitate strengthening mechanisms.