Crystal plasticity (CP) modeling is a vital tool for predicting the mechanical behavior of materials, but its calibration involves numerous (>8) constitutive parameters, often requiring time-consuming trial-and-error methods. This paper proposes a robust calibration approach using Bayesian optimization (BO) to identify optimal CP model parameters under fatigue loading conditions. Utilizing cyclic data from additively manufactured Hastelloy X specimens at 500 degree-F, the BO framework, integrated with a Gaussian process surrogate model, significantly reduces the number of required simulations. A novel objective function is developed to match experimental stress-strain data across different strain amplitudes. Results demonstrate that effective CP model calibration is achieved within 75 iterations, with as few as 50 initial simulations. Sensitivity analysis reveals the influence of CP parameters at various loading points on the stress-strain curve. The results show that the stress-strain response is predominantly controlled by parameters related to yield, with increased influence from backstress parameters during compressive loading. In addition, the effect of introducing twins into the synthetic microstructure on fatigue behavior is studied, and a relationship between microstructural features and the fatigue indicator parameter is established. Results show that larger diameter grains, which exhibit a higher Schmid factor and an average misorientation of approximately 42 degrees +/- 1.67 degree, are identified as probable sites for failure. The proposed optimization framework can be applied to any material system or CP model, streamlining the calibration process and improving the predictive accuracy of such models.

翻译：晶体塑性（CP）建模是预测材料力学行为的重要工具，但其校准涉及众多（>8个）本构参数，通常需要耗时的试错方法。本文提出一种采用贝叶斯优化（BO）的鲁棒校准方法，用于确定疲劳载荷条件下的最优CP模型参数。利用增材制造Hastelloy X试样在500华氏度下的循环数据，结合高斯过程代理模型的BO框架显著减少了所需模拟次数。本研究开发了一种新颖的目标函数，用于匹配不同应变幅值下的实验应力-应变数据。结果表明，在75次迭代内即可实现有效的CP模型校准，初始模拟次数可低至50次。敏感性分析揭示了不同加载点处CP参数对应力-应变曲线的影响规律。研究发现应力-应变响应主要受屈服相关参数控制，而在压缩加载阶段背应力参数的影响显著增强。此外，本研究探讨了在合成微观结构中引入孪晶对疲劳行为的影响，并建立了微观结构特征与疲劳指示参数之间的关联。结果表明，具有较高施密德因子且平均取向差约为42度±1.67度的大尺寸晶粒被识别为可能的失效起始位置。所提出的优化框架可适用于任何材料体系或CP模型，能有效简化和加速校准流程，并提升此类模型的预测精度。