Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/12073
Full metadata record
DC FieldValueLanguage
dc.contributor.authorManjunath, Patel, G.C.
dc.contributor.authorKrishna, P.
dc.contributor.authorParappagoudar, M.B.
dc.date.accessioned2020-03-31T08:38:38Z-
dc.date.available2020-03-31T08:38:38Z-
dc.date.issued2016
dc.identifier.citationAustralian Journal of Mechanical Engineering, 2016, Vol.14, 3, pp.182-198en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/12073-
dc.description.abstractIn the present work, an attempt has been made using statistical tools to develop a non-linear regression model and to identify the significant contribution of squeeze cast process parameters on surface roughness, hardness and tensile strength. Microstructure examination performed on the squeeze cast samples has revealed that a maximum of 100 MPa pressure is good enough to eliminate all possible casting defects. Accuracy of the developed models has been tested with the help of ten test cases. It is important to note that the developed models predict responses with a reasonably good accuracy and the developed mathematical input output relationship helps the foundry-man to make better predictions. The present work comprises four objectives, which are conflicting in nature. Hence, mathematical formulation is used to convert four objective functions into a single objective function. The popular evolutionary algorithm, that is genetic algorithm has been utilised to determine the optimal process parameters. 2015 Engineers Australia.en_US
dc.titleModelling and multi-objective optimisation of squeeze casting process using regression analysis and genetic algorithmen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.