Abstract
Convolutional neural networks (CNNs) find vast applications in the field of image processing. This study utilises the CNNs in conjunction with the crystal plasticity finite element method (CPFEM). This research presents a framework that enables CNNs to make rapid and high-fidelity predictions for materials under uniaxial tension loading. The inputs to the CNN model are material hardening parameters (initial hardness and initial hardening modulus), a global tensile strain value, and microstructure with a varying number of grains, grain size, grain morphology and texture. This input selection allows performing simulations for a wide range of materials, as defined by microstructure and flow curves. The outputs of the CNN are the local stress and strain values. The proposed framework involves the following stages: feature engineering, generation of synthetic microstructures, CPFEM simulations, data extraction and preprocessing, CNN design and selection, CNN training, and validation of the trained network. The trained CNN was successfully demonstrated to predict local stress and strain evolution for the completely new dataset (test set) containing synthesised microstructures. The test set predictions were evaluated, and the median, worst, and best predictions were presented and discussed. Overall, the CNN demonstrated excellent agreement with CPFEM simulations, thus validating its accuracy. Then, the CNN was applied to predict the stress and strain evolution for AA5754 and AA6061 microstructures obtained using electron backscatter diffraction. These two microstructures were entirely new for the CNN and displayed size and grain morphology different from the synthesised microstructures. For both microstructures, the obtained stress and strain evolution predictions demonstrated excellent agreement with CPFEM simulations, thus confirming the flexibility of the trained CNN model. Then, the framework was extended to predict strain localisation and was evaluated on an AA6061 microstructure. The results presented in this research demonstrate a clear computational advantage of CNN without loss of accuracy. Finally, the research offers prospects for future advances.