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Metamodel based multi-objective optimization of a turning process by using finite element simulation
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and automation engineering)ORCID iD: 0000-0001-7534-0382
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and automation engineering)ORCID iD: 0000-0001-5436-2128
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Materialmekanik)
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and automation engineering)ORCID iD: 0000-0003-0111-1776
(English)Article in journal (Refereed) Submitted
Abstract [en]

This study investigates the advantages and potentials of the metamodelbased multi-objective optimization (MOO) of a turning operation through the application of finite element simulations and evolutionary algorithms to a metal cutting process. The objectives are minimizing the interface temperature and tool wear depth obtained from FE simulations using DEFORM2D software, and maximizing the material removal rate. Tool geometry and process parameters are considered as the input variables. Seven metamodelling methods are employed and evaluated, based on accuracy and suitability. Radial basis functions with a priori bias and Kriging are chosen to model tool–chip interface temperature and tool wear depth, respectively. The non-dominated solutions are found using the strength Pareto evolutionary algorithm SPEA2 and compared with the non-dominated front obtained from pure simulation-based MOO. The metamodel-based MOO method is not only advantageous in terms of reducing the computational time by 70%, but is also able to discover 31 new non-dominated solutions over simulation-based MOO.

Keywords [en]
Metamodeling, Surrogate models, Machining, Turning, Multi-objective optimization
National Category
Mechanical Engineering
Research subject
Production and Automation Engineering; Mechanics of Materials
Identifiers
URN: urn:nbn:se:his:diva-15139OAI: oai:DiVA.org:his-15139DiVA, id: diva2:1204697
Note

"Preprint submitted to the Journal of Simulation Modelling Practice and Theory"

Available from: 2018-05-09 Created: 2018-05-09 Last updated: 2019-10-02Bibliographically approved
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)
National Category
Mechanical Engineering
Research subject
Production and Automation Engineering
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
Supervisors
Available from: 2018-05-14 Created: 2018-05-14 Last updated: 2019-07-04Bibliographically approved

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

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