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Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm

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dc.contributor.author Sangwan, Kuldip Singh
dc.contributor.author Garg, Girish Kant
dc.date.accessioned 2023-08-24T05:57:27Z
dc.date.available 2023-08-24T05:57:27Z
dc.date.issued 2015
dc.identifier.uri https://www.sciencedirect.com/science/article/pii/S2212827115002413
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/11616
dc.description.abstract This 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.iso en en_US
dc.publisher Elsevier en_US
dc.subject Mechanical Engineering en_US
dc.subject Roughness en_US
dc.subject Artificial Neural Networks en_US
dc.subject Genetic algorithm en_US
dc.subject Optimization en_US
dc.subject Predictive modelling en_US
dc.title Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm en_US
dc.type Article en_US


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