Accelerating the design of lattice structures using machine learning

Accelerating the design of lattice structures using machine learning

Lattices remain an attractive class of structures due to their design versatility; however, rapidly designing lattice structures with tailored or optimal mechanical properties remains a significant challenge. With each added design variable, the design space quickly becomes intractable. To address t...

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 13703 - 13
Main Authors: Gongora, Aldair E., Friedman, Caleb, Newton, Deirdre K., Yee, Timothy D., Doorenbos, Zachary, Giera, Brian, Duoss, Eric B., Han, Thomas Y.-J., Sullivan, Kyle, Rodriguez, Jennifer N.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 14-06-2024
Nature Publishing Group
Nature Portfolio
Subjects:
639/166
639/166/988
639/301/1023/303
Bayesian analysis
Computer applications
Datasets
Design
Finite element analysis
Humanities and Social Sciences
Learning algorithms
Machine learning
Mathematical models
Mechanical properties
multidisciplinary
Optimization
Physical properties
Science
Science (multidisciplinary)
Variables
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Summary:Lattices remain an attractive class of structures due to their design versatility; however, rapidly designing lattice structures with tailored or optimal mechanical properties remains a significant challenge. With each added design variable, the design space quickly becomes intractable. To address this challenge, research efforts have sought to combine computational approaches with machine learning (ML)-based approaches to reduce the computational cost of the design process and accelerate mechanical design. While these efforts have made substantial progress, significant challenges remain in (1) building and interpreting the ML-based surrogate models and (2) iteratively and efficiently curating training datasets for optimization tasks. Here, we address the first challenge by combining ML-based surrogate modeling and Shapley additive explanation (SHAP) analysis to interpret the impact of each design variable. We find that our ML-based surrogate models achieve excellent prediction capabilities ( R 2  > 0.95) and SHAP values aid in uncovering design variables influencing performance. We address the second challenge by utilizing active learning-based methods, such as Bayesian optimization, to explore the design space and report a 5 × reduction in simulations relative to grid-based search. Collectively, these results underscore the value of building intelligent design systems that leverage ML-based methods for uncovering key design variables and accelerating design.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-63204-7