An Alternative Approach for Assessing the Impact of Cutting Conditions on Surface Roughness Using Single Decision Tree
In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), 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). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130277Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 528
 C. Thomas, M. Katsuhiro, O. Toshiyuki and Y. Yasuo, “Metal Cutting”, Great Britain, 2000.
 V. A. Rogov and S. Ghorbani, “Research on selecting the optimal design of antivibrational lathe tool using computer simulation,” Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, vol. 229, no. 3, 2015, pp. 162–167.
 V. A. Rogov and S. Ghorbani, “The Effect of Tool Construction and Cutting Parameters on Surface Roughness and Vibration in Turning of AISI 1045 Steel Using Taguchi Method,” Modern Mechanical Engineering, vol. 4, 2014, pp. 8–18.
 J. Serge, “Metal Cutting Mechanics and Material Behavior,” Technische universitiet Eindhoven, 1999.
 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.
 S. Ghorbani and N. I. Polushin, “Effect of Composite Material on Damping Capacity Improvement of Cutting Tool in Machining Operation Using Taguchi Approach,” World Academy of Science, Engineering and Technology, International Journal of Chemical, Molecular, Nuclear, Materials and Metallurgical Engineering, vol. 9, no. 12, 2015, pp. 1222–1232.
 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.
 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.
 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.
 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.
 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.
 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.
 V. A. Rogov, S. Ghorbani, A. N. Popikov and N. I. Polushin, “Improvement of cutting tool performance during machining process by using different shim,” Archives of Civil and Mechanical Engineering, 10.1016/j.acme.2017.01.008.
 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.
 K. Arun Vikram and Ch. 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.
 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.
 Sivarao, T. J. S. Anand, Ammar, Shukor, “RSM based modeling for surface roughness prediction in laser machining,” International Journal of Engineering & Technology, vol. 10, no. 4, 2010, pp. 26–32.
 M. F. F. Ab. Rashid and M. R. Abdul Lani, “Surface roughness prediction for CNC milling process using artificial neural network,” Proceedings of the World Congress on Engineering, vol. 3, June 30 - July 2, 2010, London, U.K., pp. 1–6.
 Amit Kumar Gupta, “Predictive modelling 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.
 Ersan Aslan, Necip Camuscu, Burak Birgoren, “Design optimization of cutting parameters when turning hardened AISI 4140 steel (63 HRC) with Al2O3 + TiCN mixed ceramic tool,” Materials and Design, vol. 28, 2007, pp. 1618–1622.
 J. P. Davim, “A note on the determination of optimal cutting conditions for surface finish obtained in turning using design of experiments,” Journal of Materials Processing Technology, vol. 116, 2001, pp. 305–308.
 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. 59–64.
 Y. Zhao and Y. Zhang, “Comparison of decision tree methods for finding active objects,” Advances in Space Research, vol. 41, 2008, pp. 1955–1959.
 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.
 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.
 B. Filipic, M. Junkar, “Using inductive machine learning to support decision making in machining processes,” Computers in Industry, vol. 43, 2000, pp. 31–41.
 K. Wang, “An integrated intelligent process planning system (IIPPS) for machining,” Journal of Intelligent Manufacturing, vol. 9, 1998, pp. 503–514.
 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.
 P. J. Ross, “Taguchi Techniques for Quality Engineering,” McGraw-Hill International Book Company, OH, 1996.
 M. Kantardzic, Data Mining: “Concepts, models, methods, and algorithms,” John Wiley and Sons, 2003.
 J. W. Han, M. Kamber, “Data mining: concepts and techniques,” 2nd ed. CA: Morgan Kaufmann Publishers, 2001.
 J. R. Quinlan, “C4.5: Programs for machine learning, “San Mateo, (CA): Morgan Kaufman; 1993.
 D. Hand, M. Heikki, S. Padhraic, “Principles of data mining,” MIT press, 2001.
 J. R. Quinlan, “Induction of decision trees, “Machine Learning, vol. 1, 1986, pp. 81-106.
 L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone, “Classification and regression trees,” Belmont, Wadsworth Statistical Press, 1984.
 G. de’Ath and K. E. Farbricus, “Classification and regression trees: A powerful yet simple technique for ecological data, analysis,” Ecology, vol. 81, no. 11, 2000, pp. 3178–3192.
 W. Y. Loh, “Classification and regression trees, “WIREs Data Mining and Knowledge Discovery, vol. 1, 2011, pp. 14–23.
 M. K. Ayoubloo, A. Etemad-Shahidi, J. Mahjoobi, “Evaluation of regular wave scour around a circular pile using data mining approaches,” Applied Ocean Research, vol. 32, 2010, pp. 34–39.