Abstract Machine learning techniques are widely used to understand and predict data trends and therefore can provide a huge computational advantage over conventional numerical techniques. In this work, an artificial neural network (ANN) model is coupled with a rate-dependant crystal plasticity finite element method (CPFEM) formulation to predict the stress-strain behavior and texture evolution in AA6063-T6 under uniaxial tension and simple shear. Firstly, stress-strain and texture evolution results from the crystal plasticity simulations were verified with experimental observations for AA6063-T6 under simple shear and tension. Next, results from crystal plasticity simulations were used to train, validate and test the ANN model. The proposed ANN framework, was successfully applied on single crystal simulation results to predict stress-strain and texture data. Then, the proposed ANN framework was applied to predict the stress-strain curves and texture evolution of AA6063-T6 during uniaxial tension and simple shear. The flexibility of the proposed ANN model was also tested, for simple shear, with a completely new data set and the predicted results showed excellent agreement with corresponding crystal plasticity simulations. Finally, the predictive capability of the proposed model was further demonstrated by successfully validating the ANN model for non-proportional loading paths such as uniaxial tension followed by simple shear and simple shear followed by tension. The results presented in this research clearly demonstrate that the proposed ANN model provided significant computational time improvements without any major sacrifice in accuracy.

中文翻译：

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人工神经网络在多晶金属微力学中的应用

摘要 机器学习技术被广泛用于理解和预测数据趋势，因此可以提供比传统数值技术更大的计算优势。在这项工作中，人工神经网络 (ANN) 模型与速率相关晶体塑性有限元方法 (CPFEM) 公式相结合，以预测 AA6063-T6 在单轴拉伸和简单剪切下的应力-应变行为和织构演变。首先，通过简单剪切和拉伸下 AA6063-T6 的实验观察，验证了晶体塑性模拟的应力-应变和织构演变结果。接下来，晶体塑性模拟的结果用于训练、验证和测试 ANN 模型。提议的人工神经网络框架，成功应用于单晶模拟结果以预测应力应变和纹理数据。然后，将提出的 ANN 框架应用于预测 AA6063-T6 在单轴拉伸和简单剪切过程中的应力-应变曲线和织构演变。还使用全新的数据集测试了所提出的 ANN 模型的灵活性，用于简单剪切，预测结果与相应的晶体塑性模拟非常吻合。最后，通过成功验证非比例加载路径的 ANN 模型，例如单轴拉伸后简单剪切和简单剪切后拉伸，进一步证明了所提出模型的预测能力。