Cell division in deep material networks applied to multiscale strain localization modeling

• URL: http://arxiv.org/abs/2101.07226v1
• Date: Mon, 18 Jan 2021 18:24:51 GMT
• Title: Cell division in deep material networks applied to multiscale strain localization modeling
• Authors: Zeliang Liu
• Abstract summary: deep material networks (DMN) are a machine learning model with embedded mechanics in the building blocks. A new cell division scheme is proposed to track the scale transition through the network, and its consistency is ensured by the physics of fitting parameters. New crack surfaces in the cell are modeled by enriching cohesive layers, and failure algorithms are developed for crack initiation and evolution.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the increasing importance of strain localization modeling (e.g., failure analysis) in computer-aided engineering, there is a lack of effective approaches to consistently modeling related material behaviors across multiple length scales. We aim to address this gap within the framework of deep material networks (DMN) - a physics-based machine learning model with embedded mechanics in the building blocks. A new cell division scheme is proposed to track the scale transition through the network, and its consistency is ensured by the physics of fitting parameters. Essentially, each microscale node in the bottom layer is described by an ellipsoidal cell with its dimensions back-propagated from the macroscale material point. New crack surfaces in the cell are modeled by enriching cohesive layers, and failure algorithms are developed for crack initiation and evolution in the implicit DMN analysis. Besides single material point studies, we apply the multiscale model to concurrent multiscale simulations for the dynamic crush of a particle-reinforced composite tube and various tests on carbon fiber reinforced polymer composites. For the latter, experimental validations on an off-axis tensile test specimen are also provided.

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