Commenced in January 2007
Paper Count: 32131
ANFIS Modeling of the Surface Roughness in Grinding Process
Abstract:The objective of this study is to design an adaptive neuro-fuzzy inference system (ANFIS) for estimation of surface roughness in grinding process. The Used data have been generated from experimental observations when the wheel has been dressed using a rotary diamond disc dresser. The input parameters of model are dressing speed ratio, dressing depth and dresser cross-feed rate and output parameter is surface roughness. In the experimental procedure the grinding conditions are constant and only the dressing conditions are varied. The comparison of the predicted values and the experimental data indicates that the ANFIS model has a better performance with respect to back-propagation neural network (BPNN) model which has been presented by the authors in previous work for estimation of the surface roughness.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061442Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2256
 H.K. Tonshoff, "Modelling and simulation of grinding processes," Annals of the CIRP, vol. 41(2), pp. 677-688, 1992.
 J.A. Badger, A.A. Torrance, "A computer program to predict grinding forces from wheel surface profiles using slip-line fields," Proceedings of the Conf. in Adv. Man. Tech., San Sebastian, 1998.
 J.A. Badger, A.A. Torrance, "The relation between the traverse dressing of vitrified grinding wheels and their performance," Int. J. Mach. Tools & Manufacture, vol. 40, pp. 1787-1811, 2000.
 Z.B. Hou, R. Komanduri, "On the mechanics of the grinding process- Part I. Stochastic nature of the grinding process," International Journal of Mach. Tools and Manufacture, vol.43(15), pp. 1579-1593, 2003.
 H. Baseri, S.M. Rezaei, A.Rahimi, M. Saadat, "Analysis of the disc dressing effects on grinding performance PART 1: Simulation of the disc dressed wheel surface.," Mach. Sc. and Tech., vol. 12(2) , pp. 183-196, 2008.
 H. Baseri, S.M. Rezaei, A.Rahimi, M. Saadat, "Analysis of the disc dressing effects on grinding performance PART 2: Effects of the wheel topographical parameters on the specific energy and workpiece surface roughness," Mach. Sc. and Tech., vol. 12(2) , pp. 197-213, 2008.
 Y.M. Ali, L.C. Zhang, "Surface roughness prediction of ground components using a fuzzy logic approach," J. of Mat. Proc. Tech., vol. 89-90, , pp. 561-568, 1998.
 Arup Kumar Nandi, Dilip Kumar Pratihar, "Automatic design of fuzzy logic controller using a genetic algorithm to predict power requirement and surface finish in grinding," Journal of Materials Processing Technology, vol. 1489(3) , pp. 288-300, 2004.
 J.J. Govindhasamy, S. F. McLoone, G. W. Irwin, J. J. French, R. P. Doyle. "Neural modeling, control and optimization of an industrial grinding process Control," Eng. Practice, vol. 13(10) , pp. 1243-1258, 2005.
 S. Kumar, S.K. Choudhury , "Prediction of wear and surface roughness in electro-discharge diamond grinding," Journal of Materials Processing Technology, vol. 191(1-3) , pp. 206-209, 2007.
 S. Malkin, S. Guo, Grinding technology. "Theory and Applications of Machining with Abrasives," Ellis Horwood Limited, Chichester, UK, 2008.
 Jang, JSR. ANFIS: Adaptive-Network- Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics 23 (1993) 665- 685.
 H. Baseri, Workpiece Surface Roughness Prediction in Grinding Process for Different Disc Dressing Conditions, ICMET 2010, Singapore, 2010, pp209-212.