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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, Virtual Engineering Research Environment. (Produktion och Automatiseringsteknik, Production and automation engineering)ORCID iD: 0000-0001-7534-0382
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Produktion och Automatiseringsteknik, Production and automation engineering)ORCID iD: 0000-0001-5436-2128
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Virtual Manufacturing Processes)
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Produktion och Automatiseringsteknik, Production and automation engineering)ORCID iD: 0000-0003-0111-1776
2020 (English)In: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273, Vol. 52, no 7, p. 1261-1278Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2020. Vol. 52, no 7, p. 1261-1278
Keywords [en]
Metamodeling, Surrogate models, Machining, Turning, Multi-objective optimization
National Category
Mechanical Engineering
Research subject
Production and Automation Engineering; Virtual Manufacturing Processes; VF-KDO
Identifiers
URN: urn:nbn:se:his:diva-17520DOI: 10.1080/0305215X.2019.1639050ISI: 000477101800001Scopus ID: 2-s2.0-85086011026OAI: oai:DiVA.org:his-17520DiVA, id: diva2:1341953
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2023-02-22Bibliographically approved

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

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