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Metamodel based multi-objective optimization of a turning process by using finite element simulation
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och Automatiseringsteknik, Production and automation engineering)ORCID-id: 0000-0001-7534-0382
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och Automatiseringsteknik, Production and automation engineering)ORCID-id: 0000-0001-5436-2128
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Materialmekanik)
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och Automatiseringsteknik, Production and automation engineering)ORCID-id: 0000-0003-0111-1776
(engelsk)Artikkel i tidsskrift (Fagfellevurdert) 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.

Emneord [en]
Metamodeling, Surrogate models, Machining, Turning, Multi-objective optimization
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik; Materialmekanik
Identifikatorer
URN: urn:nbn:se:his:diva-15139OAI: oai:DiVA.org:his-15139DiVA, id: diva2:1204697
Merknad

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

Tilgjengelig fra: 2018-05-09 Laget: 2018-05-09 Sist oppdatert: 2019-10-02bibliografisk kontrollert
Inngår i avhandling
1. Metamodel Based Multi-Objective Optimization with Finite-Element Applications
Åpne denne publikasjonen i ny fane eller vindu >>Metamodel Based Multi-Objective Optimization with Finite-Element Applications
2018 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Högskolan i Skövde, 2018. s. 179
Serie
Dissertation Series ; 22 (2018)
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-15145 (URN)978-91-984187-4-3 (ISBN)
Disputas
2018-05-25, Portalen, Insikten, 10:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-05-14 Laget: 2018-05-14 Sist oppdatert: 2019-07-04bibliografisk kontrollert

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