عنوان انگلیسی مقاله:
Deep learning based predictive modeling for structure-property linkages
ترجمه فارسی عنوان مقاله:
مدل سازی پیش بینی مبتنی بر یادگیری عمیق برای پیوندهای ساختار و ویژگی
Sciencedirect - Elsevier - Materialia, 8 (2019) 100435: doi:10:1016/j:mtla:2019:100435
Anuradha Beniwal a , Ritesh Dadhich b , Alankar Alankar b , ∗
Crystal plasticity finite element method (CPFEM) based simulations have been traditionally used for analyses of deformation in metals. However, CPFEM simulations are computationally expensive, especially for problems like fatigue where analyses are based on deformation cycles. Moreover, correlations of structure-property linkages based on homogenization and localization are not easily conceived. In this work deep learning based models have been proposed that are able to predict macroscopic properties based on features extracted from the microstructure with minimal human bias. The model is able to predict property against a given structure within dual phase, isotropic elastic-plastic regime. A systematic approach for finding optimal depth and width of neural network has been identified that reduces the overall development effort. It is observed that in the absence of a large training dataset, performance of a convolutional neural network (CNN) model degrades if too many layers and/or too many neurons are used. The CNN model is able to identify soft and hard regions of microstructures and is able to correlate structure-property relation in forward sense i.e. for homogenization. In this work, it has been demonstrated that human intervention is not needed for feature extraction and selection leading to minimization of researcher’s bias. The drawback of CNN model interpretability is overcome by using Respond-CAM feature visualization.
Keywords: Machine learning | Crystal plasticity | Convolutional neural networks | Micromechanics | Deep learning | ICME