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Efficient Optimization of Complex Products: A Simulation and Surrogate Model Based Approach
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
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: urn:nbn:se:liu:diva-115939DOI: 10.3384/diss.diva-115939ISBN: 978-91-7519-083-9 (print)OAI: oai:DiVA.org:liu-115939DiVA: diva2:797590
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
List of papers
1. Multidisciplinary Design Optimization of Modular Industrial Robots by Utilizing High Level CAD templates
Open this publication in new window or tab >>Multidisciplinary Design Optimization of Modular Industrial Robots by Utilizing High Level CAD templates
2012 (English)In: Journal of mechanical design (1990), ISSN 1050-0472, E-ISSN 1528-9001, Vol. 134, no 12Article in journal (Refereed) Published
Abstract [en]

This paper presents a multidisciplinary design optimization (MDO) framework for automated design of a modular industrial robot. The developed design framework seamlessly integrates High Level CAD templates (HLCt) and physics based high fidelity models for automated geometry manipulation, dynamic simulation, and structural strengthanalysis. In the developed framework, methods such as surrogate models and multilevel optimization are employed in order to speed up the design optimization process. This work demonstrates how a parametric geometric model, based on the concept of HLCt, enables a multidisciplinary framework for multi-objective optimization of a modular industrial robot, which constitutes an example of a complex heterogeneous system.

Place, publisher, year, edition, pages
American Society of Mechanic, 2012
National Category
Other Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-81878 (URN)10.1115/1.4007697 (DOI)
Note

On the day of the defence day the status of this article was: Manuscript

Available from: 2012-09-24 Created: 2012-09-24 Last updated: 2017-12-07Bibliographically approved
2. Comparison of Different Uses of Metamodels for Robust Design Optimization
Open this publication in new window or tab >>Comparison of Different Uses of Metamodels for Robust Design Optimization
2013 (English)Conference paper, Published paper (Other academic)
Abstract [en]

This paper compares different approaches for using kriging metamodels for robust design optimization, with the aim of improving the knowledge of the performance of the approaches. A popular approach is to first fit a metamodel to the original model and then perform the robust design optimization on the metamodel. However, it is also possible to create metamodels during the optimization. Additionally, the metamodel need not necessarily reanimate the original model; it may also model the mean value, variance or the actual objective function. The comparisons are made with two analytical functions and a dynamic simulation model of an aircraft system as an engineering application. In the comparisons, it is seen that creating a global metamodel before the optimization begins slightly outperforms the other approaches that involve metamodels.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-84848 (URN)
Conference
51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition 7 - 10 January 2013, Texas, USA
Available from: 2012-10-24 Created: 2012-10-24 Last updated: 2015-03-24Bibliographically approved
3. Optimization of the Complex-RFM Optimization Algorithm
Open this publication in new window or tab >>Optimization of the Complex-RFM Optimization Algorithm
2015 (English)In: Optimization and Engineering, ISSN 1389-4420, E-ISSN 1573-2924, Vol. 16, no 1, 27-48 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents and compares different modifications made to the Complex-RF optimization algorithm with the aim of improving its performance for computationally expensive models. The modifications reduces the required number of objective function evaluations by creating and using surrogate models of the objective function iteratively during the optimization process. The chosen surrogate model type is a second order response surface. The performance of the modified algorithm is compared with a number of existing algorithms and demonstrated for a few analytical and engineering problems.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2015
Keyword
Optimization, Surrogate models, Meta-optimization
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-84849 (URN)10.1007/s11081-014-9247-9 (DOI)000351842300002 ()
Note

This article status has been changed from Manuscript to Article in Journal.

Available from: 2012-10-24 Created: 2012-10-24 Last updated: 2017-12-07Bibliographically approved
4. Comparisons of Different Methods for Robust Optimization in Engineering Design
Open this publication in new window or tab >>Comparisons of Different Methods for Robust Optimization in Engineering Design
(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
Robust Design Optimization, Surrogate-based optimization, Surrogate Models, Optimization
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-115940 (URN)
Available from: 2015-03-24 Created: 2015-03-24 Last updated: 2015-03-24Bibliographically approved
5. A Framework for Multidisciplinary Optimization ofa Balancing Mechanism for an Industrial Robot
Open this publication in new window or tab >>A Framework for Multidisciplinary Optimization ofa Balancing Mechanism for an Industrial Robot
2015 (English)In: Journal of Robotics, ISSN 1687-9600, E-ISSN 1687-9619, 1-8 p., 389769Article in journal (Other academic) Published
Abstract [en]

The paper presents a framework that can be used to design and optimize a balancing mechanism for an industrial robot. The framework has the capability to optimize three different concepts - a mechanical, a pneumatic and a hydro-pneumatic. Several disciplines are included in the framework, such as dynamic and static analyses of the robot performance. Optimization is performed for each concept and the obtained optimal designs are all better then the reference design. This means that the framework can be used both as a tool to optimize the balancing mechanism and also to support concept selection.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2015
Keyword
Industrial Robots, Optimization, Multi-Disciplinary, Optimization
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-115938 (URN)10.1155/2015/389769 (DOI)000361964900001 ()
Note

At the time of the thesis presentation this publication was in status Manuscript.

Available from: 2015-03-24 Created: 2015-03-24 Last updated: 2017-12-04Bibliographically approved

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