Design of mechanical metamaterials via constrained Bayesian optimization

Conner Sharpe, Carolyn Conner Seepersad, Seth Watts, Dan Tortorelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Advances in additive manufacturing processes have made it possible to build mechanical metamaterials with bulk properties that exceed those of naturally occurring materials. One class of these metamaterials is structural lattices that can achieve high stiffness to weight ratios. Recent work on geometric projection approaches has introduced the possibility of optimizing these architected lattice designs in a drastically reduced parameter space. The reduced number of design variables enables application of a new class of methods for exploring the design space. This work investigates the use of Bayesian optimization, a technique for global optimization of expensive non-convex objective functions through surrogate modeling. We utilize formulations for implementing probabilistic constraints in Bayesian optimization to aid convergence in this highly constrained engineering problem, and demonstrate results with a variety of stiff lightweight lattice designs.

Original languageEnglish (US)
Title of host publication44th Design Automation Conference
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791851753
DOIs
StatePublished - 2018
Externally publishedYes
EventASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2018 - Quebec City, Canada
Duration: Aug 26 2018Aug 29 2018

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume2A-2018

Other

OtherASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2018
Country/TerritoryCanada
CityQuebec City
Period8/26/188/29/18

ASJC Scopus subject areas

  • Mechanical Engineering
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Modeling and Simulation

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