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dc.contributor.authorGhosh, Tamal
dc.contributor.authorMartinsen, Kristian
dc.date.accessioned2021-03-02T15:16:48Z
dc.date.available2021-03-02T15:16:48Z
dc.date.created2020-04-07T16:20:39Z
dc.date.issued2020
dc.identifier.citationCommunications in Computer and Information Science. 2020, 185-195.en_US
dc.identifier.issn1865-0929
dc.identifier.urihttps://hdl.handle.net/11250/2731236
dc.description.abstractComputer Numerical Controlled (CNC) end milling processes require very complex and expensive experimentations or simulations to measure the overall performance due to the involvement of many process parameters. Such problems are computationally expensive, which could be efficiently solved using surrogate driven evolutionary optimization algorithms. An attempt is made in this paper to use such technique for the end milling process optimization of aluminium block and solved using Non-dominated Sorting Genetic Algorithm (NSGA III). The material removal rate, and surface roughness are considered as the crucial performance criteria. It is shown that the regression driven NSGA III is efficient and effective while obtaining improved process responses for the end milling.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.titleNSGA III for CNC End Milling Process Optimizationen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber185-195en_US
dc.source.journalCommunications in Computer and Information Scienceen_US
dc.identifier.doi10.1007/978-981-15-4301-2_16
dc.identifier.cristin1805592
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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