Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/16761
Full metadata record
DC FieldValueLanguage
dc.contributor.authorM C K.R.
dc.contributor.authorMalghan R.L.
dc.contributor.authorShettigar A.K.
dc.contributor.authorRao S.S.
dc.contributor.authorHerbert M.A.
dc.date.accessioned2021-05-05T10:31:35Z-
dc.date.available2021-05-05T10:31:35Z-
dc.date.issued2020
dc.identifier.citationAustralian Journal of Mechanical Engineering , Vol. , , p. -en_US
dc.identifier.urihttps://doi.org/10.1080/14484846.2020.1740022
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/16761-
dc.description.abstractThe paper explores the potential study of artificial neural network (ANN) for prediction of response surface roughness (Ra) in face milling operation with respect to cryogenic approach. The model of Ra was expressed as the main factor in face milling of spindle speed, feed rate, depth of cut and coolant type. The ANN is trained using four various back propagation algorithms (BPA). The emphasis of the paper is to investigate the performance and the accuracy of the attained results depicts the effectiveness of the trained ANN in identifying the predicted Ra. The incorporated various BPA in predicting the Ra. The performance comparative study is made among statistical (Response Surface Methodology (RSM)) and ANN (BPA–training algorithm) methods. The various incorporated BPA algorithms are Gradient Descent (GD), Scaled Conjugate Gradient Descent (SCGD), Levenberg Marquardt (LM) and Bayesian Neural Network (BNN). Afterwards the best suitable BPA is identified in predicting Ra for AISI 316 in face milling operation using liquid nitrogen (LN2) as cutting fluid. The outperformed BPA is identified based on the attained deviation percentage and time required for the training the network. © 2020, © 2020 Engineers Australia.en_US
dc.titleApplication of back propagation algorithms in neural network based identification responses of AISI 316 face milling cryogenic machining techniqueen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.