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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
Title: Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm
Authors: Sangwan, Kuldip Singh
Garg, Girish Kant
Keywords: Mechanical Engineering
Roughness
Artificial Neural Networks
Genetic algorithm
Optimization
Predictive modelling
Issue Date: 2015
Publisher: Elsevier
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.
URI: https://www.sciencedirect.com/science/article/pii/S2212827115002413
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/11616
Appears in Collections:Department of Mechanical engineering

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