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A framework for simulation based multi-objective optimization and knowledge discovery of machining process
University of Skövde, School of Engineering Science.ORCID iD: 0000-0001-7534-0382
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.ORCID iD: 0000-0001-5436-2128
University of Skövde, The Virtual Systems Research Centre.
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.ORCID iD: 0000-0003-0111-1776
2018 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 98, no 9-12, p. 2469-2486Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2018. Vol. 98, no 9-12, p. 2469-2486
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:his:diva-15136DOI: 10.1007/s00170-018-2360-8ISI: 000444704300020Scopus ID: 2-s2.0-85049664435OAI: oai:DiVA.org:his-15136DiVA, id: diva2:1204696
Available from: 2018-05-09 Created: 2018-05-09 Last updated: 2018-10-29
In thesis
1. Metamodel Based Multi-Objective Optimization with Finite-Element Applications
Open this publication in new window or tab >>Metamodel Based Multi-Objective Optimization with Finite-Element Applications
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

As a result of the increase in accessibility of computational resources and the increase of computer power during the last two decades, designers are able to create computer models to simulate the behavior of complex products. To address global competitiveness, companies are forced to optimize the design of their products and production processes. Optimizing the design and production very often need several runs of computationally expensive simulation models. Therefore, integrating metamodels, as an efficient and sufficiently accurate approximate of the simulation model, with optimization algorithms is necessary. Furthermore, in most of engineering problems, more than one objective function has to be optimized, leading to multi-objective optimization(MOO). However, the urge to employ metamodels in MOO, i.e., metamodel based MOO (MB-MOO), is more substantial.Radial basis functions (RBF) is one of the most popular metamodeling methods. In this thesis, a new approach to constructing RBF with the bias to beset a priori by using the normal equation is proposed. The performance of the suggested approach is compared to the classic RBF and four other well-known metamodeling methods, in terms of accuracy, efficiency and, most importantly, suitability for integration with MOO evolutionary algorithms. It has been found that the proposed approach is accurate in most of the test functions, and it was the fastest compared to other methods. Additionally, the new approach is the most suitable method for MB-MOO, when integrated with evolutionary algorithms. The proposed approach is integrated with the strength Pareto evolutionary algorithm (SPEA2) and applied to two real-world engineering problems: MB-MOO of the disk brake system of a heavy truck, and the metal cutting process in a turning operation. Thereafter, the Pareto-optimal fronts are obtained and the results are presented. The MB-MOO in both case studies has been found to be an efficient and effective method. To validate the results of the latter MB-MOO case study, a framework for automated finite element (FE) simulation based MOO (SB-MOO) of machining processes is developed and presented by applying it to the same metal cutting process in a turning operation. It has been proved that the framework is effective in achieving the MOO of machining processes based on actual FE simulations.

Place, publisher, year, edition, pages
Högskolan i Skövde, 2018. p. 179
Series
Dissertation Series ; 22 (2018)
Identifiers
urn:nbn:se:his:diva-15145 (URN)978-91-984187-4-3 (ISBN)
Public defence
2018-05-25, Portalen, Insikten, 10:00 (English)
Opponent
Available from: 2018-05-14 Created: 2018-05-14 Last updated: 2018-05-14Bibliographically approved

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Amouzgar, KavehBandaru, SunithAndersson, Tobias J.Ng, Amos H. C.

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