Multi-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm

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dc.contributor.authorMumtaz, Jabir
dc.contributor.authorLi, Zhang
dc.contributor.authorImran, Muhammad
dc.contributor.authorYue, Lei
dc.contributor.authorJahanzaib, Mirza
dc.contributor.authorSarfraz, Shoaib
dc.contributor.authorShehab, Essam
dc.contributor.authorIsmail, Sikiru Oluwarotimi
dc.contributor.authorAfzal, Kaynat
dc.date.accessioned2019-09-17T10:07:51Z
dc.date.available2019-09-17T10:07:51Z
dc.date.issued2019-04-22
dc.description.abstractParametric modelling and optimisation play an important role in choosing the best or optimal cutting conditions and parameters during machining to achieve the desirable results. However, analysis of optimisation of minimum quantity lubrication–assisted milling process has not been addressed in detail. Minimum quantity lubrication method is very effective for cost reduction and promotes green machining. Hence, this article focuses on minimum quantity lubrication–assisted milling machining parameters on AISI 1045 material surface roughness and power consumption. A novel low-cost power measurement system is developed to measure the power consumption. A predictive mathematical model is developed for surface roughness and power consumption. The effects of minimum quantity lubrication and machining parameters are examined to determine the optimum conditions with minimum surface roughness and minimum power consumption. Empirical models are developed to predict surface roughness and power of machine tool effectively and accurately using response surface methodology and multi-objective optimisation genetic algorithm. Comparison of results obtained from response surface methodology and multi-objective optimisation genetic algorithm depict that both measured and predicted values have a close agreement. This model could be helpful to select the best combination of end-milling machining parameters to save power consumption and time, consequently, increasing both productivity and profitability.en_UK
dc.identifier.citationMumtaz J, Li Z, Imran M, Yue L, Jahanzaib M, Sarfraz S, Shehab E, Ismail SO and Afzal K. Multi-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm. Advances in Mechanical Engineering, Volume 11, Issue 4, 2019, pp.1-13en_UK
dc.identifier.issn1687-8132
dc.identifier.urihttps://doi.org/10.1177/1687814019829588
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/14539
dc.language.isoenen_UK
dc.publisherSageen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectResponse surface methodologyen_UK
dc.subjectBox-Behnken designen_UK
dc.subjectmulti-objective genetic algorithmen_UK
dc.subjectend-millingen_UK
dc.subjectmachining parametersen_UK
dc.subjectsurface roughnessen_UK
dc.subjectpower consumptionen_UK
dc.titleMulti-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithmen_UK
dc.typeArticleen_UK

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