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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/11616
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dc.contributor.authorSangwan, Kuldip Singh-
dc.contributor.authorGarg, Girish Kant-
dc.date.accessioned2023-08-24T05:57:27Z-
dc.date.available2023-08-24T05:57:27Z-
dc.date.issued2015-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S2212827115002413-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/11616-
dc.description.abstractThis paper develops a predictive and optimization model by coupling the two artificial intelligence approaches – artificial neural network and genetic algorithm – as an alternative to conventional approaches in predicting the optimal value of machining parameters leading to minimum surface roughness. A real machining experiment has been referred in this study to check the capability of the proposed model for prediction and optimization of surface roughness. The results predicted by the proposed model indicate good agreement between the predicted values and experimental values. The analysis of this study proves that the proposed approach is capable of determining the optimum machining parameters.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectMechanical Engineeringen_US
dc.subjectRoughnessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectGenetic algorithmen_US
dc.subjectOptimizationen_US
dc.subjectPredictive modellingen_US
dc.titlePredictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithmen_US
dc.typeArticleen_US
Appears in Collections:Department of Mechanical engineering

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