Document Type : Research Paper


1 Production Engineering and Metallurgy Department, University of Technology-Iraq.

2 Prosthetics and Orthotics Engineering Department, College of Engineering, Al-Nahrain University,


The main objective of this paper is to develop a prediction model using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) for the turning process of Aluminum alloy 6061 round rod. The turning experiments carried out based on the Central Composite Design (CCD) of Response Surface Methodology. The influence of three independent variables such as Cutting speed (150, 175 and 200 mm/ min), depth of cut (0.5, 1 and 1.5 mm) and feed rate (0.1, 0.2 and 0.3 mm/rev) on the Surface Roughness (Ra) were analyzed through analysis of variance (ANOVA). The response graphs from the Analysis of Variance (ANOVA) present that feed-rate has the strongest influence on Ra dependent on cutting speed and depth of cut. Surface response methodology developed between the machining parameters and response and confirmation experiments reveals that the good agreement with the regression models. The coefficient of determination value for RSM model is found to be high (R2 = 0.961). It indicates the goodness of fit for the model and high significance of the model. From the result, the maximum error between the experimental value and ANN model is less than the RSM model significantly. However, if the test patterns number will be increased then this error can be further minimized. The proposed RSM and ANN prediction model sufficiently predict Ra accurately. However, ANN prediction model is found to be better compared to RSM model. The artificial neutral network is applied to experimental results to find prediction results for two response parameters. The predicted results taken from ANN show a good agreement between experimental and predicted values with the mean squared error of training indices equal to (0.000) which produces flexibility to the manufacturing industries to select the best setting based on applications.


[1] A. Batwara and P. Verma, “Influence of process parameters on surface roughness and material removal rate during turning in CNC lathe – an artificial neural network and surface response methodology,” International Journal of Recent advances in Mechanical Engineering (IJMECH), Vol.5, No.1, 2016.
[2] P. Jayaraman, L. Mahesh Kumar and V.S. Senthil kumar, “Optimization of cutting parameters in turning of AA6351 using response surface methodology and genetic algorithm,” International Journal of Applied Engineering Research, Vol.10, No.23, pp.43905-43911, 2015.
[3] N.Z. basha and S. vivek, “Optimization of CNC turning process parameters on ALUMINIUM 6061 using response surface methodology,” IRACST – Engineering Science and Technology: An International Journal (ESTIJ), Vol. XXX, No. XXX, 2013.
[4] R.M. Singari, Vipin and Harshit, “Optimization of process parameters in turning operation using response surface methodology: a review,” International Journal of Emerging Technology and Advanced Engineering, Vol.4, Issue 10, 2014.
[5] B. Das, S. Roy, R.N. Rai and S.C. Saha, “Studies on effect of cutting parameters on surface roughness of Al- Cu-Tic Mmcs: An Artificial Neural Network Approach,” International Conference on Advanced Computing Technologies and Applications, pp. 745-752, 2015.
[6] A. Zerti , M.A. Yallese, O. Zerti , M. Nouioua and R. Khettabi, “Prediction of machining performance using RSM and ANN models in hard turning of martensitic stainless steel AISI 420,” Journal of Mechanical Engineering Science, Vol. 0, No. 0, pp. 1–24, 2019.
[7] S.V. Alagarsamy and N. Rajakumar, “Analysis of influence of turning process parameters on MRR & surface roughness of AA7075 using Taguchi’s method and RSM,” International Journal of Applied Research and Studies (iJARS), ISSN: 2278-9480, Vol.3, Issue 4, 2014.
[8] V. Devkumar, E. Sreedhar and M.P. Prabakaran, “Optimization of machining parameters on AL 6061 alloy using response surface methodology,” International Journal of Applied Research, Vol.1, No.7, pp.01-04, 2015.
[9] R. Rudrapati, P. Sahoo and A. Bandyopadhyay, “Optimization of process parameters in CNC turning of aluminum alloy using hybrid RSM cum TLBO approach,” IOP Conf. Series: Materials Science and Engineering, 2016.
[10] A.K. Sahoo, A.K. Rout and D.K. Das, “Response surface and artificial neural network prediction model and optimization for surface roughness in machining,” International Journal of Industrial Engineering Computations, pp. 229–240, 2015.
[11] R.H. Myers, D.C. Montgomery and C.M. Anderson-Cook, “Response surface methodology process and product optimization using designed experiments,” John Wiley & Sons Publication, Third Edition, 2009.
[12] J.M. ZURADA, “Introduction to artificial neural systems,” west publishing company, 1992.