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Integrating Materials and Manufacturing Innovation
Published in: Integrating Materials and Manufacturing Innovation 2/2022

06-04-2022 | Technical Article

Data-Driven Multi-Scale Modeling and Optimization for Elastic Properties of Cubic Microstructures

Authors: M. Hasan, Y. Mao, K. Choudhary, F. Tavazza, A. Choudhary, A. Agrawal, P. Acar

Published in: Integrating Materials and Manufacturing Innovation | Issue 2/2022

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Abstract

The present work addresses gradient-based and machine learning (ML)-driven design optimization methods to enhance homogenized linear and nonlinear properties of cubic microstructures. The study computes the homogenized properties as a function of underlying microstructures by linking atomistic-scale and meso-scale models. Here, the microstructure is represented by the orientation distribution function that determines the volume densities of crystallographic orientations. The homogenized property matrix in meso-scale is computed using the single-crystal property values that are obtained by density functional theory calculations. The optimum microstructure designs are validated with the available data in the literature. The single-crystal designs, as expected, are found to provide the extreme values of the linear properties, while the optimum values of the nonlinear properties could be provided by single or polycrystalline microstructures. However, polycrystalline designs are advantageous over single crystals in terms of better manufacturability. With this in mind, an ML-based sampling algorithm is presented to identify top optimum polycrystal solutions for both linear and nonlinear properties without compromising the optimum property values. Moreover, an inverse optimization strategy is presented to design microstructures for prescribed values of homogenized properties, such as the stiffness constant (\(C_{11}\)) and in-plane Young’s modulus (\(E_{11}\)). The applications are presented for aluminum (Al), nickel (Ni), and silicon (Si) microstructures.
previous article A Comparison of Statistically Equivalent and Realistic Microstructural Representative Volume Elements for Crystal Plasticity Models
next article Data-Driven Modeling of Mechanical Properties for 17-4 PH Stainless Steel Built by Additive Manufacturing
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Metadata
Title
Data-Driven Multi-Scale Modeling and Optimization for Elastic Properties of Cubic Microstructures
Authors
M. Hasan
Y. Mao
K. Choudhary
F. Tavazza
A. Choudhary
A. Agrawal
P. Acar
Publication date
06-04-2022
Publisher
Springer International Publishing
Published in
Integrating Materials and Manufacturing Innovation / Issue 2/2022
Print ISSN: 2193-9764
Electronic ISSN: 2193-9772
DOI
https://doi.org/10.1007/s40192-022-00258-3

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