Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32146
A Comparison of Single of Decision Tree, Decision Tree Forest and Group Method of Data Handling to Evaluate the Surface Roughness in Machining Process

Authors: S. Ghorbani, N. I. Polushin


The machinability of workpieces (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron) in turning operation has been carried out using different types of cutting tool (conventional, cutting tool with holes in toolholder and cutting tool filled up with composite material) under dry conditions on a turning machine at different stages of spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). Experimentation was performed as per Taguchi’s orthogonal array. To evaluate the relative importance of factors affecting surface roughness the single decision tree (SDT), Decision tree forest (DTF) and Group method of data handling (GMDH) were applied.

Keywords: Decision Tree Forest, GMDH, surface roughness, taguchi method, turning process.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 811


[1] Y. Mustafa and T. Ali, “Determination and optimization of the effect of cutting parameters and workpiece length on the geometric tolerances and surface roughness in turning operation,” International Journal of the Physical Sciences. vol. 6, no. 5, 2011, pp. 1074–1084.
[2] M. T. Hayajneh, M. S. Tahat, J. Bluhm, “A study of the effects of machining parameters on the surface roughness in the end-milling process,” Jordan Journal of Mechanical and Industrial Engineering, vol. 1, no. 1, 2007, pp. 1–5.
[3] L. Chen, “Study on prediction of surface quality in machining process,” J. Mater. Process. Technol., vol. 205, 2008, pp. 439–450.
[4] Y. Sahin and A. R. Motorcu, “Surface roughness model for machining mild steel with coated carbide tool,” J. Mater. & Design, vol, 26, 2005, pp. 321–326.
[5] G. Mustafa and Y. Emre, “Application of Taguchi method for determining optimum surface roughness in turning of high-alloy white cast iron,” Measurement, Vol. 46, No. 2, 2013, pp. 913–919.
[6] B. M. Gopalsamy, B. Mondal, S. Ghosh, “Taguchi method and ANOVA: an approach for process parameters optimization of hard machining while machining hardened steel,” J. Sci. Ind. Res., vol. 68, 2009, no.8, pp. 686–695.
[7] E. Aslan, N. Camuscu, B. Birgoren, “Design optimization of cutting parameters when turning hardened AISI 4140 steel (63 HRC) with Al2O3 + TiCN mixed ceramic tool,” Mater. Des., vol. 28, 2007, pp. 1618–1622.
[8] S. Neseli, S. Yaldız, E. Turkes, “Optimization of tool geometry parameters for turning operations based on the response surface methodology,” Measurement, vol. 44, no. 3, 2011, pp. 580–587.
[9] A. Kacal, M. Gulesin, “Determination of optimal cutting conditions in finish turning of austempered ductile iron using Taguchi design method,” J. Sci. Ind. Res., vol. 70, 2011, pp. 278–283.
[10] K. K. Rama and J. Srinivas, “Study of tool dynamics with a discrete model of workpiece in orthogonal turning,” International Journal of Machining and Machinability of Materials, vol. 10, no. 1-2, 2011, pp. 71–85.
[11] M. Dogra, V. S. Sharma and J. Dureja, “Effect of tool geometry variation on finish turning – A Review,” Journal of Engineering Science and Technology Review, vol. 4, no. 1, 2011, pp. 1–13.
[12] K. Yusuke, M. S. Doruk, A. Yusuf, S. Norikzau and S. Eiji, “Chatter stability in turning and milling with in process identified process damping,” Journal of Advanced Mechanical Design, Systems and Manufacturing, vol. 4, no. 6, 2010, pp. 1107–1118.
[13] L. V. Martinez, J. C. Jauregui-Correa and E. Rubio-Cerda, “Analysis of compliance between the cutting tool and the workpiece on the stability of a turning process,” Int. J. Mach. Tool Manu., vol. 48, 2008, pp. 1054–1062.
[14] E. O. Ezugwu, “Key improvements in the machining of difficult-to-cut aerospace superalloys,” Int. J. Mach. Tool Manu., 2005, Vol. 45, No 12-13, pp. 1353–1367.
[15] D. Liu and J. W. Sutherland, “Active vibration abatement in a turning process by applying a magnetostrictively actuated tool holder,” Proceeding of ASME, Manufacturing Science and Engineering Division, Vol. 8, 1998, pp. 131–140.
[16] S. Kanase and V. Jadhav, “Enhancement of surface finish of boring operation using passive damper,” Indian Journal of Applied Research, vol. 2, no. 3, 2012, pp. 68–70.
[17] S. S. Abuthakeer, P. V. Mohanram and G. Mohan Kumar, “Prediction and control of cutting tool vibration cnc lathe with anova and ann,” International Journal of Lean Thinking, vol. 2, no. 1, 2011, pp. 1–23.
[18] K. Ramesh and T. Alwarsamy, ”Investigation of modal analysis in the stability of boring tool using double impact dampers model development,” European Journal of Scientific Research, vol. 80, no. 2, 2012, pp. 182–190.
[19] L. N. Devin and A. A. Osaghchii, “Improving performance of cbn cutting tools by increasing their damping properties,” Journal of Superhard Materials, vol. 34, no. 5, 2012, pp. 326–335.
[20] A. H. El-Sinawi, “Two-dimensional vibration suppression in turning using optimal control of the cutting tool,” International Journal of Machining and Machinability of Materials, vol. 3, no. 1-2, 2008, pp. 91–103.
[21] A. Piratelli-Filho, F. Levy-Neto, “Behavior of graniteepoxy composite beams subjected to mechanical vibrations,” Mater. Res., vol.13, no. 4, 2010, pp. 497–503.
[22] A. Selvakumar and P. V. Mohanram, “Analysis of alternative composite material for high speed precision machine tool structures,” Ann. Fac. Eng. Hunedoara Int. J. Eng., vol. 10, no. 2, 2012, pp. 95–98.
[23] J. Antony, “Design of experiments for engineers and scientists,” Elsevier Science & Technology Books, 2003.
[24] P. J. Ross, Taguchi techniques for quality engineering,” McGraw-Hill International Book Company, OH, 1996.
[25] P.S. Kunwar, G. Shikha and R. Premanjali, “Identifying pollution sources and predicting urban air quality using ensemble learning methods,” Atmospheric Environment, vol. 80, 2013, pp. 426–437.
[26] Efron B. Bootstrap Methods: another Look at the Jackknife. The Annals of Statistics. 1979, vol. 7, no. 1, pp. 1–26.
[27] Erdal H.I., Karakurt O. Advancing Monthly Stream Flow Prediction Accuracy of CART Models Using Ensemble Learning Paradigms. Journal of Hydrology, 2013, Vol. 477, pp. 119–128.
[28] G. Wang, J. Hao, J. Ma, H. Jiang. “A comparative assessment of ensemble learning for credit scoring,” Expert Systems with Applications, 2011, Vol. 38, No. 1, рр. 223–230.
[29] Khaled Assalah, Tamer Shanableh, Yasmeen Abu Kheil, “System Identification of magneto-rheological damper using group method of data handling (GMDH),” Proceeding of 6 th International Symposium on Mechatronic and its Applications, Sharjah, UAE, March 24-26, 2009, pp. 1–6.
[30] S. J., Farlow, “Self-Organizing Methods in Modeling. GMDH Type Algorithms,” vol. 54, Marcel Dekker Inc., New York, NY., 1984.
[31] F. H. Fernández and F. H. Lozano, “GMDH algorithm implanted in the intelligent identification of a bioprocess,” ABCM Symposium Series in Mechatronics, vol. 4, 2010, pp.278–287.
[32] A. G. Ivakhnenko, “Polynomial theory of complex systems,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 1, no. 4, 1971, pp. 364–378.
[33] K. A. Vikram and C. Ratnam, “Empirical model for surface roughness in hard turning based on analysis of machining parameters and hardness values of various engineering materials,” International Journal of Engineering Research and Application, vol. 2, no. 3, 2012, pp.3091–3097.
[34] L. B. Abhang and M. Hameedullah, “Optimal machining parameters for achieving the desired surface roughness in turning of steel,” The Journal of Engineering Research, vol. 9, no. 1, 2012, 37–45.
[35] I. Sivarao, T. J. S. Anand, Ammar and Shukor, “RSM based modeling for surface roughness prediction in laser machining,” International Journal of Engineering & Technology, vol. 10, no. 4, 2010, pp. 26–32.
[36] M. F. F. Ab. Rashid and M. R. A. Lani, “Surface roughness prediction for CNC milling process using artificial neural network,” in Proceedings of the World Congress on Engineering, Vol. 3, June 30 - July 2, 2010, London, U.K., pp. 1–6.
[37] A. Kumar Gupta, “Predictive modeling of turning operations using response surface methodology, artificial neural networks and support vector regression,” International Journal of Production Research, vol. 48, no. 3, 2010, pp. 763–778.