How to cite this paper
Sahoo, A., Rout, A & Das, D. (2015). Response surface and artificial neural network prediction model and optimization for surface roughness in machining.International Journal of Industrial Engineering Computations , 6(2), 229-240.
Refrences
Bagci, E., & I??k, B. (2006). Investigation of surface roughness in turning unidirectional GFRP composites by using RS methodology and ANN. The International Journal of Advanced Manufacturing Technology, 31(1-2), 10-17.
Che Haron, C. H., Ghani, J. A., & Ibrahim, G. A. (2007). Surface integrity of AISI D2 when turned using coated and uncoated carbide tools. International Journal of Precision Technology, 1(1), 106-114.
Dabnun, M. A., Hashmi, M. S. J., & El-Baradie, M. A. (2005). Surface roughness prediction model by design of experiments for turning machinable glass–ceramic (Macor). Journal of Materials Processing Technology, 164, 1289-1293.
Davim, J. P., Gaitonde, V. N., & Karnik, S. R. (2008). Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models. Journal of materials processing technology, 205(1), 16-23.
Feng, C. X. (2001). An experimental study of the impact of turning parameters on surface roughness. In Proceedings of the Industrial Engineering Research Conference (No. 2036, pp. 1-7).
Gillibrand, D., Sarwar, M., & Pierce, C. T. (1996). The economic benefit of finish turning with coated carbide. Surface and Coatings Technology, 86, 809-813.
G?kkaya, H., & Nalbant, M. (2007a). 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, 30(5), 307-316.
G?kkaya, H., & Nalbant, M. (2007b). The effects of cutting tool geometry and processing parameters on the surface roughness of AISI 1030 steel. Materials & design, 28(2), 717-721.
Horng, J. T., Liu, N. M., & Chiang, K. T. (2008). Investigating the machinability evaluation of Hadfield steel in the hard turning with Al2O3TiC mixed ceramic tool based on the response surface methodology. Journal of materials processing technology, 208(1), 532-541.
Karayel, D. (2009). Prediction and control of surface roughness in CNC lathe using artificial neural network. Journal of materials processing technology,209(7), 3125-3137.
Lalwani, D. I., Mehta, N. K., & Jain, P. K. (2008). Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel. Journal of materials processing technology, 206(1), 167-179.
Montgomery, D.C. (1997). Design and Analysis of Experiments. 4th ed. Wiley, New York.
Nalbant, M., G?kkaya, H., & Sur, G. (2007). Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning.Materials & design, 28(4), 1379-1385.
Nalbant, M., Gokkaya, H., & Toktas, I. (2007). Comparison of regression and artificial neural network models for surface roughness prediction with the cutting parameters in CNC turning. Modelling and Simulation in Engineering, 2007(2), 3.
Noordin, M. Y., Venkatesh, V. C., Chan, C. L., & Abdullah, A. (2001). Performance evaluation of cemented carbide tools in turning AISI 1010 steel. Journal of Materials Processing Technology, 116(1), 16-21.
Noordin, M. Y., Venkatesh, V. C., Sharif, S., Elting, S., & Abdullah, A. (2004). Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel. Journal of Materials Processing Technology, 145(1), 46-58.
?zel, T., Karpat, Y., Figueira, L., & Davim, J. P. (2007). Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts. Journal of materials processing technology, 189(1), 192-198.
?zel, T., & Karpat, Y. (2005). Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools and Manufacture, 45(4), 467-479.
Park, Y. W. (2002). Tool material dependence of hard turning on the surface quality. International Journal of Precision Engineering and Manufacturing, 3(1), 76-82.
Pal, S. K., & Chakraborty, D. (2005). Surface roughness prediction in turning using artificial neural network. Neural Computing & Applications, 14(4), 319-324.
Quiza, R., Figueira, L., & Davim, J. P. (2008). Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel. The International Journal of Advanced Manufacturing Technology,37(7-8), 641-648.
Risbood, K. A., Dixit, U. S., & Sahasrabudhe, A. D. (2003). Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. Journal of Materials Processing Technology,132(1), 203-214.
Sahin, Y., & Motorcu, A. R. (2008). Surface roughness model in machining hardened steel with cubic boron nitride cutting tool. International Journal of Refractory Metals and Hard Materials, 26(2), 84-90.
Sahoo, A. K., & Sahoo, B. (2011). Surface roughness model and parametric optimization in finish turning using coated carbide insert: Response surface methodology and Taguchi approach. International journal of industrial engineering computations, 2(4), 819-830.
Sahoo, A., Orra, K., & Routra, B. (2013). Application of response surface methodology on investigating flank wear in machining hardened steel using PVD TiN coated mixed ceramic insert. International Journal of Industrial Engineering Computations, 4(4), 469-478.
Sahoo, A., & Mohanty, T. (2013). Optimization of multiple performance characteristics in turning using Taguchi’s quality loss function: An experimental investigation. International Journal of Industrial Engineering Computations, 4(3), 325-336.
Sahoo, A. (2014). Application of Taguchi and regression analysis on surface roughness in machining hardened AISI D2 steel. International Journal of Industrial Engineering Computations, 5(2), 295-304.
Sehgal, A.K., (2013) Application of artificial neural network and response surface methodology for achieving desired surface roughness in end milling process of ductile iron grade 80-55-06, International Journal of Computational Engineering & Management, 16 (3), 2230-7893.
Suresh, P. V. S., Venkateswara Rao, P., & Deshmukh, S. G. (2002). A genetic algorithmic approach for optimization of surface roughness prediction model. International Journal of Machine Tools and Manufacture, 42(6), 675-680.
Tsao, C. C., & Hocheng, H. (2008). Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network. Journal of materials processing technology, 203(1), 342-348.
T?g?t, R., Findik, F., & Cel?k, E. (2009). Performance of multilayer coated carbide tools when turning cast iron. Turkish Journal of Engineering & Environmental Sciences, 33(3), 147 – 157.
Sharma, V. S., Dhiman, S., Sehgal, R., & Sharma, S. K. (2008). Estimation of cutting forces and surface roughness for hard turning using neural networks. Journal of Intelligent Manufacturing, 19(4), 473-483.
Che Haron, C. H., Ghani, J. A., & Ibrahim, G. A. (2007). Surface integrity of AISI D2 when turned using coated and uncoated carbide tools. International Journal of Precision Technology, 1(1), 106-114.
Dabnun, M. A., Hashmi, M. S. J., & El-Baradie, M. A. (2005). Surface roughness prediction model by design of experiments for turning machinable glass–ceramic (Macor). Journal of Materials Processing Technology, 164, 1289-1293.
Davim, J. P., Gaitonde, V. N., & Karnik, S. R. (2008). Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models. Journal of materials processing technology, 205(1), 16-23.
Feng, C. X. (2001). An experimental study of the impact of turning parameters on surface roughness. In Proceedings of the Industrial Engineering Research Conference (No. 2036, pp. 1-7).
Gillibrand, D., Sarwar, M., & Pierce, C. T. (1996). The economic benefit of finish turning with coated carbide. Surface and Coatings Technology, 86, 809-813.
G?kkaya, H., & Nalbant, M. (2007a). 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, 30(5), 307-316.
G?kkaya, H., & Nalbant, M. (2007b). The effects of cutting tool geometry and processing parameters on the surface roughness of AISI 1030 steel. Materials & design, 28(2), 717-721.
Horng, J. T., Liu, N. M., & Chiang, K. T. (2008). Investigating the machinability evaluation of Hadfield steel in the hard turning with Al2O3TiC mixed ceramic tool based on the response surface methodology. Journal of materials processing technology, 208(1), 532-541.
Karayel, D. (2009). Prediction and control of surface roughness in CNC lathe using artificial neural network. Journal of materials processing technology,209(7), 3125-3137.
Lalwani, D. I., Mehta, N. K., & Jain, P. K. (2008). Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel. Journal of materials processing technology, 206(1), 167-179.
Montgomery, D.C. (1997). Design and Analysis of Experiments. 4th ed. Wiley, New York.
Nalbant, M., G?kkaya, H., & Sur, G. (2007). Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning.Materials & design, 28(4), 1379-1385.
Nalbant, M., Gokkaya, H., & Toktas, I. (2007). Comparison of regression and artificial neural network models for surface roughness prediction with the cutting parameters in CNC turning. Modelling and Simulation in Engineering, 2007(2), 3.
Noordin, M. Y., Venkatesh, V. C., Chan, C. L., & Abdullah, A. (2001). Performance evaluation of cemented carbide tools in turning AISI 1010 steel. Journal of Materials Processing Technology, 116(1), 16-21.
Noordin, M. Y., Venkatesh, V. C., Sharif, S., Elting, S., & Abdullah, A. (2004). Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel. Journal of Materials Processing Technology, 145(1), 46-58.
?zel, T., Karpat, Y., Figueira, L., & Davim, J. P. (2007). Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts. Journal of materials processing technology, 189(1), 192-198.
?zel, T., & Karpat, Y. (2005). Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools and Manufacture, 45(4), 467-479.
Park, Y. W. (2002). Tool material dependence of hard turning on the surface quality. International Journal of Precision Engineering and Manufacturing, 3(1), 76-82.
Pal, S. K., & Chakraborty, D. (2005). Surface roughness prediction in turning using artificial neural network. Neural Computing & Applications, 14(4), 319-324.
Quiza, R., Figueira, L., & Davim, J. P. (2008). Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel. The International Journal of Advanced Manufacturing Technology,37(7-8), 641-648.
Risbood, K. A., Dixit, U. S., & Sahasrabudhe, A. D. (2003). Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. Journal of Materials Processing Technology,132(1), 203-214.
Sahin, Y., & Motorcu, A. R. (2008). Surface roughness model in machining hardened steel with cubic boron nitride cutting tool. International Journal of Refractory Metals and Hard Materials, 26(2), 84-90.
Sahoo, A. K., & Sahoo, B. (2011). Surface roughness model and parametric optimization in finish turning using coated carbide insert: Response surface methodology and Taguchi approach. International journal of industrial engineering computations, 2(4), 819-830.
Sahoo, A., Orra, K., & Routra, B. (2013). Application of response surface methodology on investigating flank wear in machining hardened steel using PVD TiN coated mixed ceramic insert. International Journal of Industrial Engineering Computations, 4(4), 469-478.
Sahoo, A., & Mohanty, T. (2013). Optimization of multiple performance characteristics in turning using Taguchi’s quality loss function: An experimental investigation. International Journal of Industrial Engineering Computations, 4(3), 325-336.
Sahoo, A. (2014). Application of Taguchi and regression analysis on surface roughness in machining hardened AISI D2 steel. International Journal of Industrial Engineering Computations, 5(2), 295-304.
Sehgal, A.K., (2013) Application of artificial neural network and response surface methodology for achieving desired surface roughness in end milling process of ductile iron grade 80-55-06, International Journal of Computational Engineering & Management, 16 (3), 2230-7893.
Suresh, P. V. S., Venkateswara Rao, P., & Deshmukh, S. G. (2002). A genetic algorithmic approach for optimization of surface roughness prediction model. International Journal of Machine Tools and Manufacture, 42(6), 675-680.
Tsao, C. C., & Hocheng, H. (2008). Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network. Journal of materials processing technology, 203(1), 342-348.
T?g?t, R., Findik, F., & Cel?k, E. (2009). Performance of multilayer coated carbide tools when turning cast iron. Turkish Journal of Engineering & Environmental Sciences, 33(3), 147 – 157.
Sharma, V. S., Dhiman, S., Sehgal, R., & Sharma, S. K. (2008). Estimation of cutting forces and surface roughness for hard turning using neural networks. Journal of Intelligent Manufacturing, 19(4), 473-483.