Comparison of Regression and Artificial Neural Network Models for Surface Roughness Prediction with the Cutting Parameters in CNC Turning

Nalbant, Muammer; Gokkaya, Hasan; Toktaş, İhsan

doi:https://doi.org/10.1155/2007/92717

Modelling and Simulation in Engineering

On this page

Abstract References Copyright Related Articles

Research Article | Open Access

Volume 2007 | Article ID 092717 | https://doi.org/10.1155/2007/92717

Comparison of Regression and Artificial Neural Network Models for Surface Roughness Prediction with the Cutting Parameters in CNC Turning

Muammer Nalbant,¹Hasan Gokkaya,²and İhsan Toktaş¹

Academic Editor: Andrzej Dzielinski

Received29 Oct 2006

Accepted03 Apr 2007

Published14 Jun 2007

Abstract

Surface roughness, an indicator of surface quality, is one of the most specified customer requirements in machining of parts. In this study, the experimental results corresponding to the effects of different insert nose radii of cutting tools (0.4, 0.8, 1.2 mm), various depth of cuts (0.75, 1.25, 1.75, 2.25, 2.75 mm), and different feedrates (100, 130, 160, 190, 220 mm/min) on the surface quality of the AISI 1030 steel workpieces have been investigated using multiple regression analysis and artificial neural networks (ANN). Regression analysis and neural network-based models used for the prediction of surface roughness were compared for various cutting conditions in turning. The data set obtained from the measurements of surface roughness was employed to and tests the neural network model. The trained neural network models were used in predicting surface roughness for cutting conditions. A comparison of neural network models with regression model was carried out. Coefficient of determination was 0.98 in multiple regression model. The scaled conjugate gradient (SCG) model with 9 neurons in hidden layer has produced absolute fraction of variance (R2) values of 0.999 for the training data, and 0.998 for the test data. Predictive neural network model showed better predictions than various regression models for surface roughness. However, both methods can be used for the prediction of surface roughness in turning.

References

E. P. DeGarmo, J. T. Black, and R. A. Kohser, Materials and Processes in Manufacturing, Maxell Macmillan, New York, NY, USA, 7th edition, 1990.
View at: Google Scholar
B. Lahidji, “Determining deflection for metal turning operations,” Journal of Industrial Technology, vol. 13, no. 2, pp. 21–23, 1997.
View at: Google Scholar
T. Özel and Y. Karpat, “Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks,” International Journal of Machine Tools and Manufacture, vol. 45, no. 4-5, pp. 467–479, 2005.
View at: Publisher Site | Google Scholar
S. A. Coker and Y. C. Shin, “In-process control of surface roughness due to tool wear using a new ultrasonic system,” International Journal of Machine Tools and Manufacture, vol. 36, no. 3, pp. 411–422, 1996.
View at: Publisher Site | Google Scholar
W. S. Lin, B. Y. Lee, and C. L. Wu, “Modeling the surface roughness and cutting force for turning,” Journal of Materials Processing Technology, vol. 108, no. 3, pp. 286–293, 2001.
View at: Publisher Site | Google Scholar
C. X. Feng, “An experimental study of the impact of turning parameters on surface roughness,” in Proceedings of the Industrial Engineering Research Conference, Dallas, Tex, USA, May 2001, paper no: 2036.
View at: Google Scholar
X. Wang and C. X. Feng, “Development of empirical models for surface roughness prediction in finish turning,” International Journal of Advanced Manufacturing Technology, vol. 20, no. 5, pp. 348–356, 2002.
View at: Publisher Site | Google Scholar
K. A. Risbood, U. S. Dixit, and A. D. Sahasrabudhe, “Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process,” Journal of Materials Processing Technology, vol. 132, no. 1–3, pp. 203–214, 2003.
View at: Publisher Site | Google Scholar
M. S. Lou, J. C. Chen, and C. M. Li, “Surface roughness prediction technique for CNC end-milling,” Journal of Industrial Technology, vol. 15, no. 1, pp. 1–6, 1998.
View at: Google Scholar
I. A. Choudhury and M. A. El-Baradie, “Machinability assessment of inconel 718 by factorial design of experiment coupled with response surface methodology,” Journal of Materials Processing Technology, vol. 95, no. 1–3, pp. 30–39, 1999.
View at: Publisher Site | Google Scholar
ISO 4287, “Geometrical Product Specifications (GPS) - Surface texture: Profile method - Terms, definitions and surface texture parameters,” 1997.
View at: Google Scholar
PDI Webmaster, “Surface Metrology Guide,” August 2004, http://www.predev.com/smg/parameters.htm.
View at: Google Scholar
J. L. Yang and J. C. Chen, “A systematic approach for identifying optimum surface roughness performance in end-milling operations,” Journal of Industrial Technology, vol. 17, no. 2, pp. 1–8, 2001.
View at: Google Scholar
D. M. Allen and F. B. Man, Analyzing Experimental Data by Regression, Lifetime Learning, Belmont, Calif, USA, 1982.
View at: Google Scholar
D. C. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, New York, NY, USA, 5th edition, 2001.
View at: Google Scholar
E. Öztemel, Integrating expert systems and neural networks for intelligent on-line statistical process control, Ph.D. thesis, School of Electrical, Electronic and Systems Engineering, University of Wales, Cardiff, Wales, UK, December 1992.
View at: Google Scholar
A. Palau, E. Velo, and L. Puigjaner, “Use of neural networks and expert systems to control a gas/solid sorption chilling machine,” International Journal of Refrigeration, vol. 22, no. 1, pp. 59–66, 1999.
View at: Publisher Site | Google Scholar
D. D. Massie, “Neural network fundamentals for scientists and engineers,” in Proceedings of the International Congress on Efficiency, Costs, Optimization, Simulation and Environmental Aspects of Energy Systems and Processes (ECOS '01), pp. 123–128, Istanbul, Turkey, July 2001.
View at: Google Scholar
M. T. Hagan and H. B. Demuth, Neural Network Design, PWS, Boston, Mass, USA, 1996.
View at: Google Scholar
E. Öztemel, Integrating expert systems and neural networks for intelligent on-line statistical process control, Ph.D. thesis, School of Electrical, Electronic and Systems Engineering, University of Wales, Cardiff, Wales, UK, December 1992.
View at: Google Scholar
M. T. Hagan and H. B. Demuth, Neural Network Design, PWS, Boston, Mass, USA, 1996.
View at: Google Scholar
MATLAB 6.5 (Release 13), The Language of Technical Computing, The MathWorks, Natick, Mass, USA, 2002.
View at: Google Scholar
E. Arcaklioǧlu, “Performance comparison of CFCs with their substitutes using artificial neural network,” International Journal of Energy Research, vol. 28, no. 12, pp. 1113–1125, 2004.
View at: Publisher Site | Google Scholar
A. Sözen, E. Arcaklıoğlu, M. Özalp, and N. Çağlar, “Forecasting based on neural network approach of solar potential in Turkey,” Renewable Energy, vol. 30, no. 7, pp. 1075–1090, 2005.
View at: Publisher Site | Google Scholar
A. Sözen, E. Arcaklıoğlu, and M. Özkaymak, “Turkey's net energy consumption,” Applied Energy, vol. 81, no. 2, pp. 209–221, 2005.
View at: Publisher Site | Google Scholar
İ. Toktaş and N. Aktürk, “A new approach using artificial neural networks for conceptual design of cylindirical helicel gears,” in Proceedings of the 1st International Vocational and Technical Education Technologies Congress (MTET '05), pp. 754–762, Istanbul, Turkey, September 2005.
View at: Google Scholar

Copyright

Copyright © 2007 Muammer Nalbant et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation Order printed copies

Views

816

Downloads

2105

Citations