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Multi-Objective Optimization of a Disc Brake System by using SPEA2 and RBFN
Jönköping University, School of Engineering, JTH. Research area Product Development - Simulation and Optimization.ORCID iD: 0000-0001-7534-0382
Jönköping University, School of Engineering, JTH. Research area Product Development - Simulation and Optimization.
Jönköping University, School of Engineering, JTH. Research area Product Development - Simulation and Optimization.
2013 (English)In: ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference: Volume 3B: 39th Design Automation ConferencePortland, Oregon, USA, August 4–7, 2013, New York: American Society of Mechanical Engineers , 2013Conference paper, Published paper (Other academic)
Abstract [en]

Many engineering design optimization problems involve multiple conflicting objectives, which today often are obtained by computational expensive finite element simulations. Evolutionary multi-objective optimization (EMO) methods based on surrogate modeling is one approach of solving this class of problems. In this paper, multi-objective optimization of a disc brake system to a heavy truck by using EMO and radial basis function networks (RBFN) is presented. Three conflicting objectives are considered. These are: 1) minimizing the maximum temperature of the disc brake, 2) maximizing the brake energy of the system and 3) minimizing the mass of the back plate of the brake pad. An iterative Latin hypercube sampling method is used to construct the design of experiments (DoE) for the design variables. Next, thermo-mechanical finite element analysis of the disc brake, including frictional heating between the pad and the disc, is performed in order to determine the values of the first two objectives for the DoE. Surrogate models for the maximum temperature and the brake energy are created using RBFN with polynomial biases. Different radial basis functions are compared using statistical errors and cross validation errors (PRESS) to evaluate the accuracy of the surrogate models and to select the most accurate radial basis function. The multi-objective optimization problem is then solved by employing EMO using the strength Pareto evolutionary algorithm (SPEA2). Finally, the Pareto fronts generated by the proposed methodology are presented and discussed.

Place, publisher, year, edition, pages
New York: American Society of Mechanical Engineers , 2013.
Keywords [en]
Multi-objective Optimization, Disc Brake, RBF, RBFN, Surrogate Modelling, Response Surface, Pareto-front
National Category
Applied Mechanics
Identifiers
URN: urn:nbn:se:hj:diva-21587DOI: 10.1115/DETC2013-12809ISBN: 978-0-7918-5589-8 (print)OAI: oai:DiVA.org:hj-21587DiVA, id: diva2:632709
Conference
ASME 2013 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2013 August 4-7, 2013, Portland, OR, USA
Available from: 2013-06-25 Created: 2013-06-25 Last updated: 2015-12-02Bibliographically approved
In thesis
1. Metamodel based multi-objective optimization
Open this publication in new window or tab >>Metamodel based multi-objective optimization
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Jönköping: Jönköping University, School of Engineering, 2015. p. 25
Series
JTH Dissertation Series ; 13
Keywords
Multi-objective optimization, strength Pareto evolutionary algorithm, SPEA2, metamodel, surrogate model, response surface, radial basis functions, RBF
National Category
Computer Engineering Mechanical Engineering
Identifiers
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
Supervisors
Available from: 2015-12-02 Created: 2015-12-02 Last updated: 2018-01-10Bibliographically approved

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