Surface Roughness Modeling in High Speed Hard Turning Using Regression Analysis
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Surface roughness plays an important role in the final quality of the machining parts. Therefore, predicting and simulating the roughness before the machining process is an important issue. The purpose of this research is to develop a reliable model for predicting and simulating the average surface roughness (Ra) in high speed hard turning. An experimental investigation was conducted to predict the surface roughness in the finish hard turning with higher cutting speed. A set of sparse experimental data for finish turning of hardened steel (AISI 4340) and mixed ceramic inserts made up of aluminum oxide and titanium carbide were used as work piece and cutting tools materials. Four different models for the surface roughness were developed by using regression analysis and artificial neural network techniques. Two different techniques have been used in the regression analysis; Box Behnken Design (BBD) and Face Central Cubic Design (FCC). The BBD model gave better prediction than the FCC in the design boundary
Copyright © 2014 Praise Worthy Prize - All rights reserved.
B., Ozcelik, & M. Bayramoglu, (2006). The statistical modeling of surface roughness in high-speed flat end milling. International Journal of Machine Tools and Manufacture, 46(12), 1395-1402.
P,Thangavel and V.Selladurai, 2008An experimental investigation on the effect of turning parameters on surface roughness. Int J Manuf Res;3: 285–300.
B., Fnides , M.A. Yallese, T. Mabrouki and J.F.Rigal (2009). Surface roughness model in turning hardened hot work steel using mixed ceramic tool. ISSN 1392 - 1207. MECHANIKA. 2009. Nr.3 (77)
Ibrahim, G.A., Che Haron, C.H., Ghani, J.A., Arshad, H., Taguchi optimization method for surface roughness and material removal rate in turning of Ti-6Al-4V ELI, (2010) International Review of Mechanical Engineering (IREME), 4 (3), pp. 216-221.
A. M., Ali, E. Y. T., Adesta, D., Agusman, S. N. M., Badari, & M.H.F. Al Hazza, (2011). Development of Surface Roughness Prediction Model for High Speed End Milling of Hardened Tool Steel. Asian Journal of Scientific Research, 4, 255-263.
E. Y. T Adesta,., M.H.F., Al Hazza, M. Suprianto, Y., & Riza, M. (2012). Predicting Surface Roughness with Respect to Process Parameters Using Regression Analysis Models in End Milling. Advanced Materials Research, 576, 99-102.
Mahesh, G., Muthu, S., Devadasan, S.R., Experimentation and prediction of surface roughness of the machining parameter with reference to the rake angle in end mill, (2012) International Review of Mechanical Engineering (IREME), 6 (7), pp. 1418-1428.
Hadi, H., Tajul, L., Hamzas, M.F.M.A., Hussin, M.S., Zailani, Z.A., Radzi, M.H.M., Parameter optimization for JIS S45C steel turning process based on Taguchi method, (2012) International Review of Mechanical Engineering (IREME), 6 (3), pp. 462-467.
Davis, R., Alazhari, M., Zubir, A.H.A., Optimization of surface roughness in wet turning operation of High Carbon High Chromium Steel, (2013) International Review of Mechanical Engineering (IREME), 7 (3), pp. 495-498.
F. J., Pontes, A. P. D., Paiva, P. P., Balestrassi, J. R., Ferreira, & M. B. D. Silva, (2012). Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi’s orthogonal arrays. Expert Systems with Applications, 39(9), 7776-7787.
M. C., Cakir, C., Ensarioglu, & Demirayak, I. (2009). Mathematical modeling of surface roughness for evaluating the effects of cutting parameters and coating material. Journal of materials processing technology, 209(1), 102-109.
F., Dweiri, M., Al-Jarrah, &, H. Al-Wedyan (2003). Fuzzy surface roughness modeling of CNC down milling of Alumic-79. Journal of Materials Processing Technology, 133(3), 266-275.
Al Hazza, M.H.F., Adesta, E.Y., Superianto, M. Y., & Riza, M. (2012a, November). Cutting Temperature and Surface Roughness Optimization in CNC End Milling Using Multi Objective Genetic Algorithm. In Advanced Computer Science Applications and Technologies (ACSAT), 2012 International Conference on (pp. 275-278). IEEE.
Al Hazza, M.H.F., Adesta, E. Y. T., Riza, M., & Suprianto, M. Y. (2012b). Surface Roughness Optimization in End Milling Using the Multi Objective Genetic Algorithm Approach. Advanced Materials Research, 576, 103-106.
D. Karayel, (2009). Prediction and control of surface roughness in CNC lathe using artificial neural network. Journal of materials processing technology, 209(7), 3125-3137.
Al Hazza, M. H., & Adesta, E. Y. (2013, December). Investigation of the effect of cutting speed on the Surface Roughness parameters in CNC End Milling using Artificial Neural Network. In IOP Conference Series: Materials Science and Engineering (Vol. 53, No. 1, p. 012089). IOP Publishing.
T., Özel, & Y.Karpat, (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.
I., Asiltürk, & M. Cunkas, (2010). Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Systems with Applications. doi:10.1016/j.eswa.2010.11.041
İ. Asiltürk, (2012). Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regressions. The International Journal of Advanced Manufacturing Technology, 63(1-4), 249-257.
Ranganathan, S., Senthilvelan, T., Prediction of machining parameters of surface roughness of GFRP composite by applying ANN and RSM, (2012) International Review of Mechanical Engineering (IREME), 6 (5), pp. 1068-1073.
P. G., Benardos, & G. C. Vosniakos, (2002). Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments. Robotics and Computer-Integrated Manufacturing, 18(5), 343-354.
P., Kovac, D., Rodic, V., Pucovsky, B., Savkovic, & M.Gostimirovic, (2012). Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing. Journal of Intelligent Manufacturing, 1-8.
E. S., Gadelmawla, M. M., Koura, T. M. A., Maksoud, I. M., Elewa, & H. H. Soliman, (2002). Roughness parameters. Journal of Materials Processing Technology, 123(1), 133-145.
- There are currently no refbacks.
Please send any question about this web site to firstname.lastname@example.org
Copyright © 2005-2021 Praise Worthy Prize