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Spot weld reduction methods for automotive structures
Linköping University, Department of Management and Engineering. Linköping University, Faculty of Science & Engineering. Combitech AB, Sweden.
Linköping University, Department of Management and Engineering, Solid Mechanics. Linköping University, Faculty of Science & Engineering.
2016 (English)In: Structural and multidisciplinary optimization (Print), ISSN 1615-147X, E-ISSN 1615-1488, Vol. 53, no 4, 923-934 p.Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

Spot welds are commonly used to join steel sheets in automotive structures. The number and layout of these spot welds are vital for the performance of the structure. However, reducing the number of spot welds will cut both production time and cost. This article presents three different methods of reducing the number of spot welds in automotive structures: ranking-based selection, topology optimization and size optimization of a parameterized model. The methods are compared in a simple example and it is found that the latter two methods have the best potential of reducing the number of spot welds. Topology optimization requires less preparation and computational effort as compared to size optimization of a parameterized model. However, the method is primarily suitable for studies where load cases involving linear systems are judged to be most important. Otherwise, size optimization of a parameterized model is probably a better choice. The topology optimization approach is successfully demonstrated in a full-scale industrial application example and confirms that the method is useful within contemporary product development.

Place, publisher, year, edition, pages
SPRINGER , 2016. Vol. 53, no 4, 923-934 p.
Keyword [en]
Spot weld optimization; Multidisciplinary design optimization (MDO); Topology optimization; Size optimization; Metamodels; Automotive structures
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-127430DOI: 10.1007/s00158-015-1355-4ISI: 000373023800018OAI: oai:DiVA.org:liu-127430DiVA: diva2:925260
Note

Funding Agencies|Vinnova FFI project

Available from: 2016-05-01 Created: 2016-04-26 Last updated: 2017-09-14
In thesis
1. Metamodel-Based Multidisciplinary Design Optimization of Automotive Structures
Open this publication in new window or tab >>Metamodel-Based Multidisciplinary Design Optimization of Automotive Structures
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Multidisciplinary design optimization (MDO) can be used in computer aided engineering (CAE) to efficiently improve and balance performance of automotive structures. However, large-scale MDO is not yet generally integrated within automotive product development due to several challenges, of which excessive computing times is the most important one. In this thesis, a metamodel-based MDO process that fits normal company organizations and CAE-based development processes is presented. The introduction of global metamodels offers means to increase computational efficiency and distribute work without implementing complicated multi-level MDO methods.

The presented MDO process is proven to be efficient for thickness optimization studies with the objective to minimize mass. It can also be used for spot weld optimization if the models are prepared correctly. A comparison of different methods reveals that topology optimization, which requires less model preparation and computational effort, is an alternative if load cases involving simulations of linear systems are judged to be of major importance.

A technical challenge when performing metamodel-based design optimization is lack of accuracy for metamodels representing complex responses including discontinuities, which are common in for example crashworthiness applications. The decision boundary from a support vector machine (SVM) can be used to identify the border between different types of deformation behaviour. In this thesis, this information is used to improve the accuracy of feedforward neural network metamodels. Three different approaches are tested; to split the design space and fit separate metamodels for the different regions, to add estimated guiding samples to the fitting set along the boundary before a global metamodel is fitted, and to use a special SVM-based sequential sampling method. Substantial improvements in accuracy are observed, and it is found that implementing SVM-based sequential sampling and estimated guiding samples can result in successful optimization studies for cases where more conventional methods fail.

Place, publisher, year, edition, pages
Linköping, Sweden: Linköping University Electronic Press, 2017. 48 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1870
Keyword
multidisciplinary design optimization (MDO), metamodel, artificial neural network (ANN), support vector machine (SVM), sequential sampling, crashworthiness, automotive structure, spot weld optimization
National Category
Mechanical Engineering Applied Mechanics Vehicle Engineering
Identifiers
urn:nbn:se:liu:diva-140875 (URN)10.3384/diss.diva-140875 (DOI)9789176854822 (ISBN)
Public defence
2017-10-03, C3, Hus C, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Funder
VINNOVA, 2009-00314VINNOVA, 2014-01340
Available from: 2017-09-14 Created: 2017-09-14 Last updated: 2017-09-20Bibliographically approved

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Ryberg, Ann-BrittNilsson, Larsgunnar
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