This study proposes a machine learning-based constitutive model for anisotropic plasticity in sheet metals. A fully connected deep neural network (DNN) is constructed to learn the stress integration procedure under the plane stress condition. The DNN utilizes the labeled training data for feature learning, and the respective dataset is generated numerically based on the Euler-backward method for the whole loading domains with one element simulation. The DNN is trained sufficiently to learn all the incremental loading paths of the input-output stress pair by using advanced anisotropic yield functions. Its performance with anisotropy is evaluated for the predictions of r-values and normalized yield stress ratios along 0–90 ° to the rolling direction. In addition, the trained DNN is then incorporated in user material subroutine UMAT in ABAQUS/Implicit. Thereafter, the DNN-based anisotropic constitutive model is tested with a cup drawing simulation to evaluate earing profile. The obtained earing profile is compatible with the one from the trained anisotropic yield function.

本研究提出了一种基于机器学习的钣金各向异性塑性本构模型。构建了一个完全连接的深度神经网络 (DNN) 来学习平面应力条件下的应力积分过程。DNN利用标记的训练数据进行特征学习，并基于欧拉后向法对整个加载域进行单元模拟，以数值方式生成相应的数据集。DNN 经过充分训练，可以通过使用高级各向异性屈服函数来学习输入-输出应力对的所有增量加载路径。它的各向异性性能被评估用于预测 r 值和沿 0-90° 到轧制方向的归一化屈服应力比。此外，然后将经过训练的 DNN 合并到 ABAQUS/Implicit 中的用户材料子程序 UMAT 中。此后，基于 DNN 的各向异性本构模型通过杯形拉伸模拟进行测试，以评估耳形轮廓。获得的耳朵轮廓与训练的各向异性屈服函数的耳朵轮廓兼容。