Topology optimization of 2D structures with nonlinearities using deep learning

Abstract

The field of optimal design of linear elastic structures has seen many exciting successes that resulted in new architected materials and structural designs. With the availability of cloud computing, including high-performance computing, machine learning, and simulation, searching for optimal nonlinear structures is now within reach. In this study, we develop convolutional neural network models to predict optimized designs for a given set of boundary conditions, loads, and optimization constraints. We have considered the case of materials with a linear elastic response with and without stress constraint. Also, we have considered the case of materials with a hyperelastic response, where material and geometric nonlinearities are involved. For the nonlinear elastic case, the neo-Hookean model is utilized. For this purpose, we generate datasets composed of the optimized designs paired with the corresponding boundary conditions, loads, and constraints, using a topology optimization framework to train and validate the neural network models. The developed models are capable of accurately predicting the optimized designs without requiring an iterative scheme and with negligible inference computational time. The suggested pipeline can be generalized to other nonlinear mechanics scenarios and design domains.

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• Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure
• Algorithmically-Consistent Deep Learning Frameworks for Structural Topology Optimization
• Data-driven topology design using a deep generative model
• Deep energy method in topology optimization applications
• Exploring the structure-property relations of thin-walled, 2D extruded lattices using neural networks
• An AI-Assisted Design Method for Topology Optimization Without Pre-Optimized Training Data
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• Neural Networks for Generating Better Local Optima in Topology Optimization
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• Meshless physics-informed deep learning method for three-dimensional solid mechanics
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• A deep learning energy method for hyperelasticity and viscoelasticity
• On the Application of Data-Driven Deep Neural Networks in Linear and Nonlinear Structural Dynamics
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