Show simple item record

dc.contributor.authorMumtaz, J.
dc.contributor.authorLi, Z.
dc.contributor.authorImran, M.
dc.contributor.authorYue, L.
dc.contributor.authorJahanzaib, M.
dc.contributor.authorSarfraz, S.
dc.contributor.authorShehab, E.
dc.contributor.authorIsmail, S. O.
dc.contributor.authorAfzal , K.
dc.date.accessioned2019-04-25T14:00:15Z
dc.date.available2019-04-25T14:00:15Z
dc.date.issued2019-04-01
dc.identifier.citationMumtaz , J , Li , Z , Imran , M , Yue , L , Jahanzaib , M , Sarfraz , S , Shehab , E , Ismail , S O & Afzal , K 2019 , ' 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 , vol. 11 , no. 4 , pp. 1-13 . https://doi.org/10.1177/1687814019829588
dc.identifier.issn1687-8132
dc.identifier.otherPURE: 16636928
dc.identifier.otherPURE UUID: c0b13088-b096-46f1-9f10-9136bbf97390
dc.identifier.otherScopus: 85064817753
dc.identifier.otherORCID: /0000-0003-1451-1736/work/62751315
dc.identifier.urihttp://hdl.handle.net/2299/21298
dc.description© 2019 by SAGE Publications Ltd.
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
dc.format.extent13
dc.language.isoeng
dc.relation.ispartofAdvances in Mechanical Engineering
dc.titleMulti-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithmen
dc.contributor.institutionMaterials and Structures
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionSchool of Engineering and Technology
dc.description.statusPeer reviewed
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1177/1687814019829588
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record