Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)

Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various pr...

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Autor principal:	Osamah F. Abdulateef
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2021
Materias:	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/40a0b46117684703b74de8ff333be4ff
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spelling	oai:doaj.org-article:40a0b46117684703b74de8ff333be4ff2021-12-02T14:38:42ZPrediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)10.22153/kej.2021.01.0011818-11712312-0789https://doaj.org/article/40a0b46117684703b74de8ff333be4ff2021-06-01T00:00:00Zhttps://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/736https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 mm/rev and a depth of cut 0.4 mm was found to achieve lower surface roughness with, an increase in the cutting speed and feed rate with the increases of the surface roughness. In addition, an increase in the depth of cut was found to reduces the surface roughness. The outcome of this study showed that ANN is a versatile tool for prediction of surface roughness and may be easily extended with greater confidence to various metal cutting processes. Osamah F. AbdulateefAl-Khwarizmi College of Engineering – University of BaghdadarticleChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 17, Iss 2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Osamah F. Abdulateef Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)
description	Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 mm/rev and a depth of cut 0.4 mm was found to achieve lower surface roughness with, an increase in the cutting speed and feed rate with the increases of the surface roughness. In addition, an increase in the depth of cut was found to reduces the surface roughness. The outcome of this study showed that ANN is a versatile tool for prediction of surface roughness and may be easily extended with greater confidence to various metal cutting processes.
format	article
author	Osamah F. Abdulateef
author_facet	Osamah F. Abdulateef
author_sort	Osamah F. Abdulateef
title	Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)
title_short	Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)
title_full	Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)
title_fullStr	Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)
title_full_unstemmed	Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)
title_sort	prediction of surface roughness after turning of duplex stainless steel (dss)
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2021
url	https://doaj.org/article/40a0b46117684703b74de8ff333be4ff
work_keys_str_mv	AT osamahfabdulateef predictionofsurfaceroughnessafterturningofduplexstainlesssteeldss
_version_	1718390888959311872

Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)

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