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The purpose of this work is the development of an artificial neural network(ANN) for surrogate modeling of the mechanical response of viscoplastic grainmicrostructures. To this end, a U-Net-based convolutional neural network (CNN)is trained to account for the history dependence of the material behavior. Thetraining data take the form of numerical simulation results for the von Misesstress field under quasi-static tensile loading. The trained CNN (tCNN) canaccurately reproduce both the average response as well as the local von Misesstress field. The tCNN calculates the von Mises stress field of grainmicrostructures not included in the training dataset about 500 times fasterthan its calculation based on the numerical solution with a spectral solver ofthe corresponding initial-boundary-value problem. The tCNN is also successfullyapplied to other types of microstructure morphologies (e.g., matrix-inclusiontype topologies) and loading levels not contained in the training dataset.

An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials