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An evolution law for fabric anisotropy and its application in micromechanical modelling of granular materials
قانون تکامل برای ناهمسانگردی پارچه و کاربرد آن در مدل سازی میکرومکانیکی مواد دانه ای-2020 Micromechanical studies of granular materials have demonstrated the importance of their microstruc- ture to their behaviour. This microstructure is often characterized by fabric tensors. Experimental and computational studies have shown that the fabric can change significantly during deformation. Therefore, the evolution of fabric is important to constitutive modelling. Current fabric evolution laws for granular materials have generally been developed for continuum-mechanical models, and use a loading index mul- tiplier associated with a yield surface. Such evolution laws can not be employed with micromechanical models that do not involve an explicit macro-scale yield surface. This study develops an evolution law for fabric anisotropy, based on observations from experiments and Discrete Element Method simulations from literature. The proposed evolution law considers the effects of inherent anisotropy, void ratio, stress ratio, loading direction and intermediate principal stress ratio. In the critical state, the value of the fabric anisotropy depends only on the Lode angle. The predicted evolution of fabric anisotropy is in good agreement with results of Discrete Element Method simulations, showing both hardening and softening behaviour and describing the influence of the initial void ratio. The proposed evolution law can be embedded into micromechanics-based constitutive relations as well as conventional continuum-mechanical models. As an example, a well-established micromechanical model (in which the fabric is considered as constant) has been extended by accounting for the variations in fabric, in combination with the proposed fabric evolution law. The performance of this enhanced mi- cromechanical model has been demonstrated by a comparison between the predicted behaviour and ex- perimental results from literature for Toyoura sand under various loading conditions. Keywords: Granular material | Fabric | Micromechanics | Constitutive modelling |
مقاله انگلیسی |
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Deep learning based predictive modeling for structure-property linkages
مدل سازی پیش بینی مبتنی بر یادگیری عمیق برای پیوندهای ساختار و ویژگی-2019 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 |
مقاله انگلیسی |