Regression Modeling of Surface Roughness in Grinding


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Grinding is a widely used manufacturing method in state of art industry. By realizing needs of manufacturers, grinding parameters must be carefully selected in order to maintain an optimum point for sustainable process. Surface roughness is generally accepted as an important indicator for grinding parameters. In this study, effects of grinding parameters to surface roughness were experimentally and statistically investigated. A complete factorial experimental flow was designed for three level and three variable. 62 HRC AISI 8620 cementation steel was used in grinding process with 95-96% Al2O3 grinding wheel. Surface roughness values (Ra, Rz) were measured at the end of process by using depth of cut, feed rate and workpiece speed as input parameters. Experimental results were used for modeling surface roughness values with linear, quadric and logarithmic regressions by the help of MINITAB 14 and SPSS 16 software. The best results according to comparison of models considering determination coefficient were achieved with quadric regression model (84.6% for Ra and 89% for Rz). As a result, a reliable model was developed in grinding process which is a highly complex machining operation and depth of cut was determined as the most effective parameter on grinding by variance analysis (ANOVA). Obtained theoretical and practical acquisitions can be used in various areas of manufacturing sector in the future.



Advanced Materials Research (Volumes 271-273)

Edited by:

Junqiao Xiong




I. Asiltürk et al., "Regression Modeling of Surface Roughness in Grinding", Advanced Materials Research, Vols. 271-273, pp. 34-39, 2011

Online since:

July 2011




[1] İ. Asiltürk: Application of Artificial Intelligent Based Adaptive Control to Saw Cutting Process (Selçuk University, Turkey 2007).

[2] L. Çelik: Monitoring Vibration in Grinding Process and Regression Modeling of Surface Roughness (Selçuk University, Turkey 2010).

[3] P. R. Aguiar, J. A. Serni, C.B. Eduardo, R. L. Dotto: In-Process Grinding Monitoring By Acoustic Emission (University of Sao Paulo, Brazil 2004).

[4] A. Batako and S. Koppal: Process Monitoring İn High Efficiency Deep Grinding- HEDG (General Engineering Research Institute, Liverpool John Moores University, United Kingdom 2007).

[5] H. Demir and A. Güllü: Investigating Relationship Between Grinding Parameters and Surface Roughness and Grinding Rate in Cylindrical Grinding Process (Gazi University, Turkey 1995).

[6] A. Güllü: Optimization Grinding Parameters Using Computer to Obtain Desired Surface Roughness in Cylindrical Grinding (Gazi University, Turkey 1995).

[7] H. Özkara: Grading Vocation Technology III (Milli Eğitim Yayınevi, Turkey 2002).

[8] H. Demir and A. Güllü: G.Ü. Technical Education Faculty Machine Journal, Vol 198 (2000), pp.1-10.

[9] H. Demir: Investigating Effects of Grinding Parameters to Grinding Forces and Surface Quality at Flat Surface Grinding (Gazi University, Turkey 2003).

[10] X. Chen and W.B. Rowe: International Journal of Machine Tools and Manufacture, Vol 36 (8) (1996), p.871–882.

[11] X. H. Guo, M. F. Liu, C. R. Zhao: Journal Applied Mechanics and Materials, Vol 44 (2010), p.47.

[12] M. Kiyak, O. Cakir, E. Altan: AMME-2003 Proc. Of the 12th Int. Scientific Conf., pp.459-462, Gliwice-Poland, (2003).

[13] M.C. Akbulut: Homogeneity of Variances in Variance Analysis Technique with Factorial order and First Type Error About Interactions When Normal Dispersion Preconditions aren't Provided and test Power (Ankara University, Turkey 2008).

[14] İ. Demirayak, M. C. Çakır: Investigating Effects of Cutting Parameters and Coating Layer to Work Piece Surface Quality, IV. Machine Design and Manufacturing Technologies Congress, Konya (2007), Turkey.