This study presents an efficient multimaterial design optimization algorithm that is suitable for nonlinear structures. The proposed algorithm consists of three steps: conceptual design generation, design characterization by machine learning, and metamodel-based multi-objective optimization. The conceptual design can be generated from extracting finite element analysis information or by using structure optimization. The conceptual design is then characterized by using machine learning techniques to dramatically reduce the dimension of the design space. Finally, metamodels are derived using Efficient Global Optimization (EGO) followed by multi-objective design optimization to find the optimal material distribution. The proposed methodology is demonstrated using examples from multiple physics and compared with traditional multimaterial topology optimization method.
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ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 21–24, 2016
Charlotte, North Carolina, USA
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-5011-4
PROCEEDINGS PAPER
Machine Learning and Metamodel-Based Design Optimization of Nonlinear Multimaterial Structures
Duane Detwiler,
Duane Detwiler
Honda R&D Americas, Inc., Raymond, OH
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Andres Tovar
Andres Tovar
Indiana U.-Purdue U. Indianapolis, Indianapolis, IN
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Kai Liu
Purdue University, West Lafayette, IN
Duane Detwiler
Honda R&D Americas, Inc., Raymond, OH
Andres Tovar
Indiana U.-Purdue U. Indianapolis, Indianapolis, IN
Paper No:
DETC2016-60471, V02BT03A015; 10 pages
Published Online:
December 5, 2016
Citation
Liu, K, Detwiler, D, & Tovar, A. "Machine Learning and Metamodel-Based Design Optimization of Nonlinear Multimaterial Structures." Proceedings of the ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2B: 42nd Design Automation Conference. Charlotte, North Carolina, USA. August 21–24, 2016. V02BT03A015. ASME. https://doi.org/10.1115/DETC2016-60471
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