Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30127
Using Single Decision Tree to Assess the Impact of Cutting Conditions on Vibration

Authors: S. Ghorbani, N. I. Polushin

Abstract:

Vibration during machining process is crucial since it affects cutting tool, machine, and workpiece leading to a tool wear, tool breakage, and an unacceptable surface roughness. This paper applies a nonparametric statistical method, single decision tree (SDT), to identify factors affecting on vibration in machining process. Workpiece material (AISI 1045 Steel, AA2024 Aluminum alloy, A48-class30 Gray Cast Iron), cutting tool (conventional, cutting tool with holes in toolholder, cutting tool filled up with epoxy-granite), tool overhang (41-65 mm), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev) and depth of cut (0.05-0.15 mm) were used as input variables, while vibration was the output parameter. It is concluded that workpiece material is the most important parameters for natural frequency followed by cutting tool and overhang.

Keywords: Cutting condition, vibration, natural frequency, decision tree, CART algorithm.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1126563

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 894

References:


[1] C. Thomas, M. Katsuhiro, O. Toshiyuki and Y. Yasuo, “Metal cutting,” Great Britain, 2000.
[2] 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.
[3] A. A. Tareq, “Extending the technological capability of turning operation,” International Journal of Engineering, Science and Technology, vol. 2, no. 1, 2009, pp. 192–201.
[4] K. A. Samir Mahammod Hassan and G. Amro, “Investigation into the turning parameters effect on the surface roughness of flame hardened medium carbon steel with TiN-Al2O3-TiCN coated inserts based on taguchi technique,” World Academy of Science, Engineering and Technology, vol. 59, 2011, pp. 2137–2141.
[5] 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.
[6] C. M. Taylor, N. D. Sims and S. Turner. “Process damping and cutting tool geometry in machining,” IOP Conf. Series. Mater Sci Eng; vol. 2, no. 1, 2011, pp. 1–17.
[7] 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.
[8] 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.
[9] 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,” International Journal of Machine Tools and Manufacture, vol. 48, 2008, pp. 1054–1062.
[10] 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.
[11] 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.
[12] 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.
[13] I. H. Witten and E. Frank, “Data mining: practical machine learning tools and technique,” 2nd edition, Morgan Kufman, 2005.
[14] A. Aherwar1, D. Unune, B. Pathri, J. kishan, “Statistical and regression analysis of vibration of carbon steel cutting tool for turning of EN24 steel using design of experiments,” International Journal of Recent advances in Mechanical Engineering, vol.3, no.3, 2014, pp. 137–151.
[15] C. O. Izelu, S. C. Eze, B. U. Oreko, B. A Edward, “Effect of Depth of Cut, Cutting Speed, Work-piece Overhang on Induced Vibration and Surface Roughness in the Turning of 41Cr4 Alloy Steel,” International Journal of Emerging Technology and Advanced Engineering, vol. 4, no. 1, 2014, pp. 1–5.
[16] S. C. Eze, C. O. Izelu, B. U. Oreko, B. A. Edward, “Experimental study of induced vibration and work surface roughness in the turning of 41Cr4 alloy steel using response surface methodology,” International Journal of Innovative Research in Science, Engineering and Technology, vol. 2, no. 12, 2013, pp. 7677–7684.
[17] N. S. Pohokar, L. B. Bhuyar, “Optimization of tools for CNC machine: An explication & an overview,” International Journal of Scientific & Engineering Research, vol. 3, no. 12, 2012, pp. 1–9.
[18] B. Srinivasa Prasad, M.M. Sarcar, “Analysis of face milling operation using acousto optic emission and 3D surface topography of machined surfaces for in-process tool condition monitoring,” Jordan Journal of Mechanical and Industrial Engineering, vol. 5, no. 6, 2011, pp. 509–519.
[19] H. Byeon, “The risk factors of laryngeal pathology in Korean adults using a decision tree model,” Journal of Voice, vol. 29, no. 1, 2015, pp. 59–65.
[20] N. C. Coops, R. H. Waring, C. Beier, R. Roy-Jauvin, T. Wang, “Modeling the occurrence of 15coniferous tree species throughout the Pacific Northwest of North America using a hybrid approach of a generic process-based growth model and decision tree analysis,” Applied Vegetation Science, vol. 14, 2011, pp. 402–414.
[21] Y. Zhao, Y. Zhang, “Comparison of decision tree methods for finding active objects,” Advances in Space Research, vol. 41, 2008, pp. 1955–1959.
[22] I. Aydin, M. Karakose, E. Akin, “An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space,” ISA Transactions, vol. 53, 2014, pp. 220–229.
[23] M. Saimurugan, K. I. Ramachandran, V. Sugumaran, N. R. Sakthivel, “Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine,” Expert Systems with Applications, vol. 38, 2011, pp. 3819–3826.
[24] N. T. Nguyen, J. M. Kwon and H H. Lee, “A study on machine fault diagnosis using decision tree,” Journal of Electrical Engineering & Technology, vol. 2, no. 4, 2007, pp. 461-467.
[25] K. Wang, “An integrated intelligent process planning system (IIPPS) for machining,” Journal of Intelligent Manufacturing, vol. 9, 1998, pp. 503–514.
[26] P. J. Ross, “Taguchi techniques for quality engineering,” McGraw-Hill International Book Company, OH, 1996.
[27] D. Hand, M. Heikki, S. Padhraic, “Principles of data mining,” MIT press; 2001.
[28] M. Kantardzic, “Data Mining: Concepts, models, methods, and algorithms. John Wiley & Sons,” 2003.
[29] J. R. Quinlan. C4.5: Programs for machine learning. San Mateo, (CA): Morgan Kaufman; 1993.
[30] J.W. Han, M. Kamber, “Data mining: concepts and techniques,” 2rd ed. CA: Morgan Kaufmann Publishers, 2001.
[31] Quinlan JR, “Induction of decision trees,” Mach Learn 1986; Vol. 1, pp. 81-106.
[32] L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone. “Classification and regression trees. Belmont: Wadsworth Statistical,” CRC Press; 1984.
[33] W. Y. Loh, “Classification and regression trees,” WIREs Data Mining and Knowledge Discovery, vol. 1, 2011, pp. 14–23.
[34] Glenn de’Ath and Katharina E. Farbricus, “Classification and regression trees: A powerful yet simple technique for ecological data, analysis,” Ecology, vol. 81, no. 11, 2000, pp. 3178–3192.