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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/11776
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dc.contributor.authorGarg, Girish Kant-
dc.contributor.authorSangwan, Kuldip Singh-
dc.date.accessioned2023-08-31T10:12:07Z-
dc.date.available2023-08-31T10:12:07Z-
dc.date.issued2020-07-
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-030-44248-4_15-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/11776-
dc.description.abstractSurface roughness is one of the significant index to measure the product quality of the machined parts. The objective of this work is to contribute towards the development of prediction models for surface roughness. In this work, the predictive models were developed for turning operations using soft computing techniques; support vector regression (SVR) and artificial neural network (ANN). The turning experiments are conducted to obtain the experimental data. The developed predictive models were compared using relative error and validated using hypothesis testing. The results indicate that both techniques provide a close relation between the predicted values and the experimental values for surface roughness and are appropriate to predict the surface roughness with significant acceptable accuracy. It is found that ANN performs better as compared to SVR.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectMechanical Engineeringen_US
dc.subjectSurface roughnessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectSupport Vector Regressionen_US
dc.titleA Comparative Analysis of Surface Roughness Prediction Models Using Soft Computing Techniquesen_US
dc.typeArticleen_US
Appears in Collections:Department of Mechanical engineering

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