Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85293
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dc.contributor.authorGarg, A.en
dc.contributor.authorTai, K.en
dc.date.accessioned2013-08-02T04:35:44Zen
dc.date.accessioned2019-12-06T16:01:00Z-
dc.date.available2013-08-02T04:35:44Zen
dc.date.available2019-12-06T16:01:00Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.urihttps://hdl.handle.net/10356/85293-
dc.description.abstractThe mathematical modeling of machining processes has received immense attention and attracted a number of researchers because of its significant contribution to the overall cost and quality of product. The literature study demonstrates that conventional approaches such as statistical regression, response surface methodology, etc. requires physical understanding of the process for the erection of precise and accurate models. The statistical assumptions of such models induce ambiguity in the prediction ability of the model. Such limitations do not prevail in the nonconventional modeling approaches such as Genetic Programming (GP), Artificial Neural Network (ANN), Fuzzy Logic (FL), Genetic Algorithm (GA), etc. and therefore ensures trustworthiness in the prediction ability of the model. The present work discusses about the notion, application, abilities and limitations of Genetic Programming for modeling of machining processes. The characteristics of GP uncovered from the current review are compared with features of other modeling approaches applied to machining processes.en
dc.language.isoenen
dc.titleReview of genetic programming in modeling of machining processesen
dc.typeConference Paperen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen
dc.contributor.conferenceInternational Conference on Modelling, Identification & Control (2012 : Wuhan, Hubei, China)en
dc.identifier.openurlhttp://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6260225&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6260225en
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