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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. (Virtual Manufacturing Processes)
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
2019 (engelsk)Inngår i: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273Artikkel i tidsskrift (Fagfellevurdert) Epub ahead of print
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.

sted, utgiver, år, opplag, sider
Taylor & Francis Group, 2019.
Emneord [en]
Metamodeling, Surrogate models, Machining, Turning, Multi-objective optimization
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik; Virtual Manufacturing Processes
Identifikatorer
URN: urn:nbn:se:his:diva-17520DOI: 10.1080/0305215X.2019.1639050ISI: 000477101800001OAI: oai:DiVA.org:his-17520DiVA, id: diva2:1341953
Tilgjengelig fra: 2019-08-12 Laget: 2019-08-12 Sist oppdatert: 2019-11-06bibliografisk kontrollert

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Amouzgar, KavehBandaru, SunithAndersson, TobiasNg, Amos H. C.

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