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

  • Osamah F. Abdulateef Department of Automated Manufacturing Engineering / Al-Khwairzmi College of Engineering/ University of Baghdad/ Iraq

Abstract

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.

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References

J. Z. Zhang, J. C. Chen, and E. D. Kirby, “Surface roughness optimization in an end-milling operation using the Taguchi design method,” Journal of materials processing technology, vol. 184, no. 1-3, pp. 233-239, 2007. https://doi.org/10.1016/j.jmatprotec.2006.11.029

Kahraman, and A. Sagbas, “An investigation of the effect of heat treatment on surface roughness in machining by using statistical analysis,” Iranian Journal of Science & Technology, Transaction B: Engineering, vol. 34, no. B5, pp. 591-595, 2010.

H. Gokkaya, and M. Nalbant, “ The effects of cutting tool coating on the surface roughness of AISI 1015 steel depending on cutting parameters,” Turkish Journal of Engineering and Environmental Sciences, vol. 30, no. 5, pp. 307-316, 2006.

M. Nalbant, H. Gokkaya, and I. Toktas, “Comparison of regression and artificial neural network models for surface roughness prediction with the cutting parameters in CNC turning,” Modeling and Simulation in Engineering, vol. 2007, pp. 1-14, 2007. https://doi.org/10.1155/2007/92717

K. Palanikumar, and R. Karthikeyan, “Optimal machining conditions for turning of particulate metal matrix composites using Taguchi and response surface methodologies,” Machining Science and Technology: An International Journal, vol.10, no. 4, pp. 417-433, 2007. https://doi.org/10.1080/10910340600996068

A. Javidi, U. Rieger, and W. Eichlseder, “The effect of machining on the surface integrity and fatigue life,” International Journal of Fatigue, vol. 30, no. 10-11, pp. 2050-2055, 2008. https://doi.org/10.1016/j.ijfatigue.2008.01.005

H. Dave, L. Patel, and H. Raval, “Effect of machining conditions on MRR and surface roughness during CNC turning of different materials using TiN coated cutting tools- A Taguchi approach”, International Journal of Industrial Engineering Computations, vol. 3, no. 5, pp. 925-930, 2012.

K. Mani Lavanya, R. K .Suresh, A. Sushil Kumar Priya, and G. Krishnaiah, “Optimization of Process Parameters in Turning Operation of AISI-1016 Alloy Steels with CBN Using Artificial Neural Networks,” International Journal of Engineering Trends and Technology (IJETT), vol. 5, no.6, pp. 294-297, 2013.

G. Kant, and K. S. Sangwan, “Predictive modeling and optimization of machining parameters to minimize surface roughness using artificial neural network coupled with genetic algorithm," Procedia CIRP, vol. 31, pp. 453-458, 2015. https://doi.org/10.1016/j.procir.2015.03.043

J. Vaibhav, and B. Sachin, “Process parameter optimization of CNC turning for titanium wrought iron,” International Journal of Modern Trends in Engineering and Research, vol. 2, no. 11, pp. 141-146, 2015.

B. Das, R. N. Rai, , and S.C. Saha, “ Surface quality optimization of Al-5Cu alloy using utility theory coupled with the Taguchi method,” Advances in Applied Physical and Chemical Sciences-A Sustainable Approach, vol. 2015, pp. 40-45, 2015.

M. Gupta, and S. Kumar, “ Investigation of surface roughness and MRR for turning UD-GFRP using PCA and Taguchi method,” Engineering Science and Technology, An International Journal, vol. 18, no. 1, pp. 70-81, 2015. https://doi.org/10.1016/j.jestch.2014.09.006

P. Sonali, and A. M. Mohanty, “ Performance analysis of surface roughness in al alloy using different cutting parameters,” International Journal of Multidisciplinary Research and Development, vol. 3, no. 4, pp. 1-4, 2016.

S. Dahbi, L. Ezzine, and H.E.L. Moussami, “Modeling of cutting performances in turning process using artificial neural networks,” International Journal of Engineering Business Management, vol. 9, pp. 1-13, 2017. https://doi.org/10.1177%2F1847979017718988

K. Pawan and J. P. Misra, “A Surface Roughness Predictive Model for DSS Longitudinal Turning Operation,” Chapter 25 in DAAAM International Scientific Book, 2018, pp.285-296.

Arjun Joshy, Royson Dsouza, Veerakumar Muthirulan1, and Krishnamurthy H. Sachidananda, “Experimental Analysis on the Turning of Aluminum Alloy 7075 Based on Taguchi Method and Artificial Neural Network,” Journal Européen des Systèmes Automatisés, vol. 52, no. 5, pp. 429-437, 2019. https://doi.org/10.18280/jesa.520501

R. Suresh, L. Shivaramu, N.G. Siddesh Kumar, T.N. Srikantha Dath, “Effect of Process Parameters on Cutting Forces and Surface Roughness in Machining of DSS 2205 Using Taguchi’s Approach,” Applied Mechanics and Materials, vol. 895, pp. 26-31, 2019. https://doi.org/10.4028/www.scientific.net/AMM.895.26

Ahmed A. Elsadek, Ahmed M. Gaafer, and S.S. Mohamed, “Surface Roughness Prediction in Hard-Turning with ANN and RSM,” Journal of The Egyptian Society of Tribology, vol. 17, no. 2, pp. 13-22, 2020.

M. V. R. D. Prasad, Yelamanchili Sravya, and Karri Sai Tejaswi, “Study of the Influence of Process Parameters on Surface Roughness When Inconel 718 Is Dry Turned Using CBN Cutting Tool by Artificial Neural Network Approach,” International Journal of Materials, Mechanics and Manufacturing, vol. 2, no. 4, pp. 335-338, 2014.

Published
2021-06-01
How to Cite
Abdulateef, O. (2021). Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS). Al-Khwarizmi Engineering Journal, 17(2), 8-17. https://doi.org/10.22153/kej.2021.01.001