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Surrogate assisted optimization of particle reinforced metal matrix composites

Published: 02 July 2018 Publication History

Abstract

Surrogate Model Based Optimization (SMBO) is an established technique for handling computationally expensive optimization problems. One important application is the optimization of Particle Reinforced Metal Matrix Composites (PRMMCs). Multi-phase materials are gaining attention. Their performance is strongly affected by microscale properties. By optimizing the microscale structure, these materials can be tailored to satisfy specific requirements. Current manufacturing techniques have limited control over the distribution of reinforcing particles and are subject to considerable uncertainty. Moreover, the simulation and optimization of PRMMCs requires significant computational effort. We propose an approach that tackles the problem of optimizing the characteristics of PRMMCs subject to uniaxial load, by improving the particles' spatial distribution. The optimization problem is split into a bilevel problem: The upper-level optimization aims to find the particle distribution parameters which maximize the PRMMC limit load. Due to potentially infeasible distributions, the lower-level problem attempts to create a particle placement that reflects the specifications of an upper-level candidate solution.
We employ an SMBO approach that combines Kriging, Sequential Parameter Optimization, and a Genetic Algorithm. Experimental results indicate that our approach can find promising solutions within few evaluations, handles uncertainty, and allows insight into the most important effects on the limit load.

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Cited By

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  • (2021)High-Lift Devices Topology Robust Optimisation Using Machine Learning Assisted OptimisationAdvances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications10.1007/978-3-030-80542-5_18(297-313)Online publication date: 16-Jul-2021

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cover image ACM Conferences
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference
July 2018
1578 pages
ISBN:9781450356183
DOI:10.1145/3205455
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 July 2018

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Author Tags

  1. finite element methods
  2. multilevel optimization
  3. optimization under uncertainty
  4. parameter optimization
  5. surrogate model based optimization

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  • H2020 Marie SkBodowska-Curie Actions

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View all
  • (2021)High-Lift Devices Topology Robust Optimisation Using Machine Learning Assisted OptimisationAdvances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications10.1007/978-3-030-80542-5_18(297-313)Online publication date: 16-Jul-2021

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