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Multi-objective optimization of material model parameters of an adhesive layer by using SPEA2
Högskolan i Jönköping, Tekniska Högskolan, JTH, Produktutveckling. Högskolan i Jönköping, Tekniska Högskolan, JTH. Forskningsmiljö Produktutveckling - Simulering och optimering.ORCID-id: 0000-0001-7534-0382
Högskolan i Jönköping, Tekniska Högskolan, JTH, Produktutveckling. Högskolan i Jönköping, Tekniska Högskolan, JTH. Forskningsmiljö Produktutveckling - Simulering och optimering.
Högskolan i Jönköping, Tekniska Högskolan, JTH, Produktutveckling. Högskolan i Jönköping, Tekniska Högskolan, JTH. Forskningsmiljö Produktutveckling - Simulering och optimering.
2015 (engelsk)Inngår i: Advances in structural and multidisciplinary optimization: Proceedings of the 11th World Congress of Structural and Multidisciplinary Optimization (WCSMO-11) / [ed] Qing Li, Grant P Steven, Zhongpu (Leo) Zhang, The International Society for Structural and Multidisciplinary Optimization (ISSMO) , 2015, s. 249-254Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The usage of multi material structures in industry, especially in the automotive industry are increasing. To overcome the difficulties in joining these structures, adhesives have several benefits over traditional joining methods. Therefore, accurate simulations of the entire process of fracture including the adhesive layer is crucial. In this paper, material parameters of a previously developed meso mechanical finite element (FE) model of a thin adhesive layer are optimized using the Strength Pareto Evolutionary Algorithm (SPEA2). Objective functions are defined as the error between experimental data and simulation data. The experimental data is provided by previously performed experiments where an adhesive layer was loaded in monotonically increasing peel and shear. Two objective functions are dependent on 9 model parameters (decision variables) in total and are evaluated by running two FEsimulations, one is loading the adhesive layer in peel and the other in shear. The original study converted the two objective functions into one function that resulted in one optimal solution. In this study, however, a Pareto frontis obtained by employing the SPEA2 algorithm. Thus, more insight into the material model, objective functions, optimal solutions and decision space is acquired using the Pareto front. We compare the results and show good agreement with the experimental data.

sted, utgiver, år, opplag, sider
The International Society for Structural and Multidisciplinary Optimization (ISSMO) , 2015. s. 249-254
Emneord [en]
Multi-objective optimization, parameter identification, micro mechanical model, adhesive, CZM
HSV kategori
Identifikatorer
URN: urn:nbn:se:hj:diva-28422ISBN: 978-0-646-94394-7 (tryckt)OAI: oai:DiVA.org:hj-28422DiVA, id: diva2:875653
Konferanse
11th World Congress of Structural and Multidisciplinary Optimization (WCSMO-11)
Tilgjengelig fra: 2015-12-01 Laget: 2015-12-01 Sist oppdatert: 2018-09-12bibliografisk kontrollert
Inngår i avhandling
1. Finite element methods on surfaces
Åpne denne publikasjonen i ny fane eller vindu >>Finite element methods on surfaces
2015 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

The purpose of this thesis is to improve numerical simulations of surface problems. Two novel computational concepts are analyzed and applied on two surface problems; minimal surface problems and elastic membrane problems. The concept of tangential projection implies that direct computation on the surface is made possible compared to the classical approach of mapping 2D parametric surfaces to 3D surfaces by means of differential geometry operators. The second concept presented is the cut finite element method, in which the basic idea of discretization is to embed the d- 1-dimensional surface in a d-dimensional mesh and use the basis functions of a higher dimensional mesh but integrate over the surface. The aim of this thesis is to present the basics of the two main approaches and to provide details on the implementation.

sted, utgiver, år, opplag, sider
Jönköping: Jönköping University, School of Engineering, 2015. s. 41
Serie
JTH Dissertation Series ; 12
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-28424 (URN)978-91-87289-13-2 (ISBN)
Veileder
Forskningsfinansiär
Swedish Research Council, 2011-4992
Tilgjengelig fra: 2015-12-01 Laget: 2015-12-01 Sist oppdatert: 2018-01-10bibliografisk kontrollert
2. Metamodel based multi-objective optimization
Åpne denne publikasjonen i ny fane eller vindu >>Metamodel based multi-objective optimization
2015 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

As a result of the increase in accessibility of computational resources and the increase in the power of the computers during the last two decades, designers are able to create computer models to simulate the behavior of a complex products. To address global competitiveness, companies are forced to optimize their designs and products. Optimizing the design needs several runs of computationally expensive simulation models. Therefore, using metamodels as an efficient and sufficiently accurate approximate of the simulation model is necessary. Radial basis functions (RBF) is one of the several metamodeling methods that can be found in the literature.

The established approach is to add a bias to RBF in order to obtain a robust performance. The a posteriori bias is considered to be unknown at the beginning and it is defined by imposing extra orthogonality constraints. In this thesis, a new approach in constructing RBF with the bias to be set a priori by using the normal equation is proposed. The performance of the suggested approach is compared to the classic RBF with a posteriori bias. Another comprehensive comparison study by including several modeling criteria, such as problem dimension, sampling technique and size of samples is conducted. The studies demonstrate that the suggested approach with a priori bias is in general as good as the performance of RBF with a posteriori bias. Using the a priori RBF, it is clear that the global response is modeled with the bias and that the details are captured with radial basis functions.

Multi-objective optimization and the approaches used in solving such problems are briefly described in this thesis. One of the methods that proved to be efficient in solving multi-objective optimization problems (MOOP) is the strength Pareto evolutionary algorithm (SPEA2). Multi-objective optimization of a disc brake system of a heavy truck by using SPEA2 and RBF with a priori bias is performed. As a result, the possibility to reduce the weight of the system without extensive compromise in other objectives is found.

Multi-objective optimization of material model parameters of an adhesive layer with the aim of improving the results of a previous study is implemented. The result of the original study is improved and a clear insight into the nature of the problem is revealed.

sted, utgiver, år, opplag, sider
Jönköping: Jönköping University, School of Engineering, 2015. s. 25
Serie
JTH Dissertation Series ; 13
Emneord
Multi-objective optimization, strength Pareto evolutionary algorithm, SPEA2, metamodel, surrogate model, response surface, radial basis functions, RBF
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-28432 (URN)978-91-87289-14-9 (ISBN)
Presentation
2015-12-11, E1405, School of Engineering, Gjuterigatan 5, Jönköping, 14:00
Opponent
Veileder
Tilgjengelig fra: 2015-12-02 Laget: 2015-12-02 Sist oppdatert: 2018-01-10bibliografisk kontrollert

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