Metadata about: Abstract The microstructure of a material governs mechanical properties such as strength and toughness. Various finite element analysis (FEA) software packages are used to perform structural analyses such as predicting the flow of strain or strain fields in a microstructure. Engineers frequently operate these software packages to evaluate mechanical behavior and predict failure. Even though these FEA software packages provide highly accurate analyses, they are computationally intensive, taking a significant amount of time to produce a solution. The time required by the FEA software packages to achieve accurate results largely depends on microstructure details and mesh resolution, thus providing a trade-off between fidelity and computation time. This research proposes the use of Deep Learning algorithms to achieve a significant reduction in the time required to predict high-accuracy strain fields in a two-dimensional microstructure with defects. This work presents a foundation for developing deep neural networks to conduct structural analyses, thus reducing the exclusive use of computationally demanding FEA software and augmenting the analytical capabilities of scientists and engineers.

About: Abstract The microstructure of a material governs mechanical properties such as strength and toughness. Various finite element analysis (FEA) software packages are used to perform structural analyses such as predicting the flow of strain or strain fields in a microstructure. Engineers frequently operate these software packages to evaluate mechanical behavior and predict failure. Even though these FEA software packages provide highly accurate analyses, they are computationally intensive, taking a significant amount of time to produce a solution. The time required by the FEA software packages to achieve accurate results largely depends on microstructure details and mesh resolution, thus providing a trade-off between fidelity and computation time. This research proposes the use of Deep Learning algorithms to achieve a significant reduction in the time required to predict high-accuracy strain fields in a two-dimensional microstructure with defects. This work presents a foundation for developing deep neural networks to conduct structural analyses, thus reducing the exclusive use of computationally demanding FEA software and augmenting the analytical capabilities of scientists and engineers.

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