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Comparisons of Different Methods for Robust Optimization in Engineering Design
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper compares the performance of five methods for robust design optimization of computationally demanding models including one novel method. The comparison is made using several mathematical functions and two engineering problems. The performance metrics are the mean value and standard deviation of the optimum as well as an index that weights together the required number of simulations of the original model and the chance of finding the optimum. The result of the comparison shows that sequential robust optimization is the most effective method.

Keyword [en]
Robust Design Optimization, Surrogate-based optimization, Surrogate Models, Optimization
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-115940OAI: oai:DiVA.org:liu-115940DiVA: diva2:797583
Available from: 2015-03-24 Created: 2015-03-24 Last updated: 2015-03-24Bibliographically approved
In thesis
1. Efficient Optimization of Complex Products: A Simulation and Surrogate Model Based Approach
Open this publication in new window or tab >>Efficient Optimization of Complex Products: A Simulation and Surrogate Model Based Approach
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis investigates how to use optimization efficiently when complex products are developed. Modelling and simulation are necessary to enable optimization of products, but here it is assumed that verified and validated models of the products and their subsystems are available for the optimization. The focus is instead on how to use the models properly for optimization.

Knowledge about several areas is needed to enable optimization of a wide range of products. A few methods from each area are investigated and compared. Some modifications to existing methods and new methods are also proposed and compared to the previous methods.

These areas include

  • Optimization algorithms to ensure that a suitable algorithm is used to solve the problem
  • Multi-Objective Optimization for products with conflicting objectives
  • Multi-Disciplinary Optimization when analyses from several models and/or disciplines are needed
  • Surrogate Models to enable optimization of computationally expensive models

Modern frameworks for optimization of complex products often include more than one of these areas and this is exemplified with the industrial applications that are presented in this thesis, including the design and optimization of industrial robots and aircraft systems.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. 88 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1655
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-115939 (URN)10.3384/diss.diva-115939 (DOI)978-91-7519-083-9 (ISBN)
Public defence
2015-04-24, ACAS, A-huset, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework Programme, Crescendo no. 234244VINNOVA, IMPOz no. 2013-03758
Available from: 2015-03-24 Created: 2015-03-24 Last updated: 2015-04-17Bibliographically approved

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Persson, JohanÖlvander, Johan

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Citation style
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