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Optimization and robustness of structural product families
Linköping University, Department of Management and Engineering, Solid Mechanics. Linköping University, The Institute of Technology.
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis concerns structural optimization of product families and robustness. The overall objective is to find a method for performing structural optimization of product families subjected to multiple crash load cases and to ensure a robust behavior.

A product family is a family of products where every product variant, or family member, shares at least one component with at least one other product in the family. Structural optimization of such a family is complex and for expensive function evaluations, e.g. crash simulations, the computing time to solve the problem with traditional methods gets unrealistically long. Therefore, a new optimization algorithm for product families is presented, called the critical constraint method (CCM), that reduces the number of required evaluations by only considering the active constraints in the optimal solution.

Traditionally optimized structures have a tendency of being sensitive to small variations in the design or loading conditions. As these kind of variations are inevitable, it is important to account for this sensitivity in the design process to ensure the robustness of the optimized design.

The thesis is divided in two parts. The first part serves as a theoretical background to the second part, the two appended articles. This first part includes introductions to the concepts of product families, optimization, meta modeling and robust design.

The first appended paper presents a new optimization algorithm for product families subjected to multiple crash loads. The method is compared to traditional methods and tested on two smaller product family examples.

The second paper is an application of an existing sensitivity analysis method on a large industrial application example. A sensitivity analysis is performed on a Scania truck cab subjected to an impact load in order to identify the most  influencing variables on the crash responses.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press , 2009. , 50 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1403
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-17947Local ID: LIU-TEK-LIC-2009:11ISBN: 978-91-7393-626-2 (print)OAI: oai:DiVA.org:liu-17947DiVA: diva2:213052
Presentation
2009-06-10, A-huset, Campus Valla, Linköpings universitet, Linköping, 10:00 (English)
Opponent
Supervisors
Available from: 2009-10-16 Created: 2009-04-27 Last updated: 2013-10-24Bibliographically approved
List of papers
1. Structural optimization of product families subjected to multiple crash load cases
Open this publication in new window or tab >>Structural optimization of product families subjected to multiple crash load cases
2010 (English)In: Structural and multidisciplinary optimization (Print), ISSN 1615-147X, E-ISSN 1615-1488, Vol. 41, no 5, 797-815 p.Article in journal (Refereed) Published
Abstract [en]

This paper discusses the problem of structural optimization of product families subjected to multiple load cases, evaluated by computationally costly finite element analysis. Product families generally have a complex composition of shared components that makes individual product optimization difficult as the relation between the shared variables is not always intuitive. More optimal is to treat the problem as a product family optimization problem. Though, for product families subjected to multiple and computationally costly crash loads, the optimization problem takes too long time to solve with traditional methods. Therefore, a new optimization algorithm is presented that decomposes the family problem into sub-problems and iteratively reduces the number of sub-problems, decouple and solve them. The algorithm is applicable for module based product families with predefined composition of generalized commonality, subjected to multiple load cases that can be analyzed separately. The problem reduction is performed by only considering the constraints that are critical in the optimal solution. Therefore the optimization algorithm is called the Critical Constraint Method, CCM. Finally the CCM algorithm is evaluated by two product family optimization problems.

Place, publisher, year, edition, pages
Springer Science Business Media, 2010
Keyword
Product family optimization, Multiple crash loads, Nonlinear systems, Response surface methodology, Meta model approximations, Critical constraints
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-54846 (URN)10.1007/s00158-009-0471-4 (DOI)000276075900011 ()
Available from: 2010-04-16 Created: 2010-04-16 Last updated: 2017-12-12Bibliographically approved
2. Finite element based robustness study of a truck cab subjected to impact loading
Open this publication in new window or tab >>Finite element based robustness study of a truck cab subjected to impact loading
2009 (English)In: International Journal of Crashworthiness, ISSN 1358-8265, E-ISSN 1754-2111, Vol. 14, no 2, 111-124 p.Article in journal (Refereed) Published
Abstract [en]

Optimised designs have a tendency of being sensitive to variations. It is therefore of great importance to analyse this sensitivity to assure that a design is robust, i.e. sufficiently insensitive to variations. To analyse robustness, variations are introduced in model parameters and their influences on simulation responses are studied. This is usually achieved using the Monte Carlo method. Though, due to the large number of simulations needed, the Monte Carlo method is very costly for problems requiring a long computing time. Therefore, in this work a meta model-based Monte Carlo method is used to evaluate the robustness of a vehicle structure. That is, the Monte Carlo analysis is performed on a surface approximation of the true response, over the domain of interest. The methodology used is to first identify the variables that influence the response the most, referred to as a screening, using simple linear response surfaces. This is followed by a more detailed sensitivity analysis using only the identified variables and a quadratic response surface, thereby incorporating second order effects. A truck cab model exposed to a pendulum impact load is used as an evaluation of this method, and the important variables and their influence on the response are identified. The effect of including results from forming simulations is also evaluated using the truck cab model. Variations are introduced before forming simulations, thereby taking forming effects into account in the sensitivity analysis. The method was found to be a good tool to identify important dispersion variables and to give an approximate result of the total dispersion, all with a reasonable amount of simulations.

Place, publisher, year, edition, pages
Taylor & Francis, 2009
Keyword
Monte Carlo, meta model, robust design, response surface method, sensitivity analysis, robustness
National Category
Applied Mechanics
Identifiers
urn:nbn:se:liu:diva-15474 (URN)10.1080/13588260802412992 (DOI)
Projects
ROBDES
Note
On the day of the defence date the status of this article was: Accepted.Available from: 2008-11-11 Created: 2008-11-11 Last updated: 2017-12-14Bibliographically approved

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