{"title":"An Alternative Approach for Assessing the Impact of Cutting Conditions on Surface Roughness Using Single Decision Tree","authors":"S. Ghorbani, N. I. Polushin","volume":124,"journal":"International Journal of Mechanical and Mechatronics Engineering","pagesStart":825,"pagesEnd":831,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10007017","abstract":"
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.<\/p>\r\n","references":"[1]\tC. Thomas, M. Katsuhiro, O. Toshiyuki and Y. Yasuo, \u201cMetal Cutting\u201d, Great Britain, 2000.\r\n[2]\tV. A. Rogov and S. Ghorbani, \u201cResearch on selecting the optimal design of antivibrational lathe tool using computer simulation,\u201d Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, vol. 229, no. 3, 2015, pp. 162\u2013167.\r\n[3]\tV. A. Rogov and S. Ghorbani, \u201cThe Effect of Tool Construction and Cutting Parameters on Surface Roughness and Vibration in Turning of AISI 1045 Steel Using Taguchi Method,\u201d Modern Mechanical Engineering, vol. 4, 2014, pp. 8\u201318.\r\n[4]\tJ. Serge, \u201cMetal Cutting Mechanics and Material Behavior,\u201d Technische universitiet Eindhoven, 1999.\r\n[5]\tK. K. Rama and J. Srinivas, \u201cStudy of Tool Dynamics with a Discrete Model of Workpiece in Orthogonal Turning,\u201d International Journal of Machining and Machinability of Materials, vol. 10, no. 1-2, 2011, pp. 71\u201385.\r\n[6]\tS. Ghorbani and N. I. Polushin, \u201cEffect of Composite Material on Damping Capacity Improvement of Cutting Tool in Machining Operation Using Taguchi Approach,\u201d World Academy of Science, Engineering and Technology, International Journal of Chemical, Molecular, Nuclear, Materials and Metallurgical Engineering, vol. 9, no. 12, 2015, pp. 1222\u20131232.\r\n[7]\tK. Ramesh and T. Alwarsamy, \u201cInvestigation of Modal Analysis in the Stability of Boring Tool using Double Impact Dampers Model Development,\u201d European Journal of Scientific Research, vol. 80, no. 2, 2012, pp. 182\u2013190.\r\n[8]\tM. Dogra, V. S. Sharma and J. Dureja, \u201cEffect of Tool Geometry Variation on Finish Turning \u2013 A Review,\u201d Journal of Engineering Science and Technology Review, vol. 4, no. 1, 2011, pp. 1\u201313.\r\n[9]\tK. Yusuke, M. S. Doruk, A. Yusuf, S. Norikzau and S. Eiji, \u201cChatter Stability in Turning and Milling with In Process Identified Process Damping,\u201d Journal of Advanced Mechanical Design, Systems and Manufacturing, vol. 4, no. 6, 2010, pp. 1107\u20131118.\r\n[10]\tL. V. Martinez, J. C. Jauregui-Correa and E. Rubio-Cerda, \u201cAnalysis of Compliance between the Cutting Tool and The Workpiece on the Stability of a Turning Process,\u201d International Journal of Machine Tools and Manufacture, vol. 48, 2008, pp. 1054\u20131062.\r\n[11]\tS. Kanase and V. Jadhav, \u201cEnhancement of Surface Finish of Boring Operation using Passive Damper,\u201d Indian Journal of Applied Research, vol. 2, no. 3, 2012, pp. 68\u201370. \r\n[12]\tL. N. Devin and A. A. Osaghchii, \u201cImproving Performance of cBN Cutting Tools by Increasing their Damping Properties,\u201d Journal of Superhard Materials, vol. 34, no. 5, 2012, pp. 326\u2013335.\r\n[13]\tV. A. Rogov, S. Ghorbani, A. N. Popikov and N. I. Polushin, \u201cImprovement of cutting tool performance during machining process by using different shim,\u201d Archives of Civil and Mechanical Engineering, 10.1016\/j.acme.2017.01.008.\r\n[14]\tS. S. Abuthakeer, P. V. Mohanram and G. Mohan Kumar, \u201cPrediction and Control of Cutting Tool Vibration Cnc Lathe with Anova and Ann,\u201d International Journal of Lean Thinking, vol. 2, no. 1, 2011, pp. 1\u201323. \r\n[15]\tK. Arun Vikram and Ch. Ratnam, \u201cEmpirical model for surface roughness in hard turning based on analysis of machining parameters and hardness values of various engineering materials,\u201d International Journal of Engineering Research and Application, vol. 2, no. 3, 2012, pp.3091\u20133097.\r\n[16]\tL. B. Abhang and M. Hameedullah, \u201cOptimal machining parameters for achieving the desired surface roughness in turning of steel,\u201d The Journal of Engineering Research, vol. 9, no. 1, 2012, 37\u201345.\r\n[17]\tSivarao, T. J. S. Anand, Ammar, Shukor, \u201cRSM based modeling for surface roughness prediction in laser machining,\u201d International Journal of Engineering & Technology, vol. 10, no. 4, 2010, pp. 26\u201332.\r\n[18]\tM. F. F. Ab. Rashid and M. R. Abdul Lani, \u201cSurface roughness prediction for CNC milling process using artificial neural network,\u201d Proceedings of the World Congress on Engineering, vol. 3, June 30 - July 2, 2010, London, U.K., pp. 1\u20136.\r\n[19]\tAmit Kumar Gupta, \u201cPredictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression,\u201d International Journal of Production Research, vol. 48, no. 3, 2010, pp. 763\u2013778.\r\n[20]\tErsan Aslan, Necip Camuscu, Burak Birgoren, \u201cDesign optimization of cutting parameters when turning hardened AISI 4140 steel (63 HRC) with Al2O3 + TiCN mixed ceramic tool,\u201d Materials and Design, vol. 28, 2007, pp. 1618\u20131622.\r\n[21]\tJ. P. Davim, \u201cA note on the determination of optimal cutting conditions for surface finish obtained in turning using design of experiments,\u201d Journal of Materials Processing Technology, vol. 116, 2001, pp. 305\u2013308.\r\n[22]\tH. Byeon, \u201cThe Risk Factors of Laryngeal Pathology in Korean Adults Using a Decision Tree Model,\u201d Journal of Voice. vol. 29, no. 1, 2015, pp. 59\u201364.\r\n[23]\tY. Zhao and Y. Zhang, \u201cComparison of decision tree methods for finding active objects,\u201d Advances in Space Research, vol. 41, 2008, pp. 1955\u20131959.\r\n[24]\tN. C. Coops, R. H.Waring, C. Beier, R. Roy-Jauvin, T. Wang, \u201cModeling 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,\u201d Applied Vegetation Science, vol. 14, 2011, pp. 402\u2013414.\r\n[25]\tM. Saimurugan, K. I. Ramachandran, V. Sugumaran, N. R. Sakthivel, \u201cMulti component fault diagnosis of rotational mechanical system based on decision tree and support vector machine,\u201d Expert Systems with Applications, Vol. 38, 2011, pp. 3819\u20133826.\r\n[26]\tB. Filipic, M. Junkar, \u201cUsing inductive machine learning to support decision making in machining processes,\u201d Computers in Industry, vol. 43, 2000, pp. 31\u201341.\r\n[27]\tK. Wang, \u201cAn integrated intelligent process planning system (IIPPS) for machining,\u201d Journal of Intelligent Manufacturing, vol. 9, 1998, pp. 503\u2013514.\r\n[28]\tI. Aydin, M. Karakose, E. Akin, \u201cAn approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space,\u201d ISA Transactions, vol. 53, 2014, pp. 220\u2013229.\r\n[29]\tP. J. Ross, \u201cTaguchi Techniques for Quality Engineering,\u201d McGraw-Hill International Book Company, OH, 1996.\r\n[30]\tM. Kantardzic, Data Mining: \u201cConcepts, models, methods, and algorithms,\u201d John Wiley and Sons, 2003. \r\n[31]\tJ. W. Han, M. Kamber, \u201cData mining: concepts and techniques,\u201d 2nd ed. CA: Morgan Kaufmann Publishers, 2001.\r\n[32]\tJ. R. Quinlan, \u201cC4.5: Programs for machine learning, \u201cSan Mateo, (CA): Morgan Kaufman; 1993.\r\n[33]\tD. Hand, M. Heikki, S. Padhraic, \u201cPrinciples of data mining,\u201d MIT press, 2001.\r\n[34]\tJ. R. Quinlan, \u201cInduction of decision trees, \u201cMachine Learning, vol. 1, 1986, pp. 81-106.\r\n[35]\tL. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone, \u201cClassification and regression trees,\u201d Belmont, Wadsworth Statistical Press, 1984.\r\n[36]\tG. de\u2019Ath and K. E. Farbricus, \u201cClassification and regression trees: A powerful yet simple technique for ecological data, analysis,\u201d Ecology, vol. 81, no. 11, 2000, pp. 3178\u20133192.\r\n[37]\tW. Y. Loh, \u201cClassification and regression trees, \u201cWIREs Data Mining and Knowledge Discovery, vol. 1, 2011, pp. 14\u201323.\r\n[38]\tM. K. Ayoubloo, A. Etemad-Shahidi, J. Mahjoobi, \u201cEvaluation of regular wave scour around a circular pile using data mining approaches,\u201d Applied Ocean Research, vol. 32, 2010, pp. 34\u201339.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 124, 2017"}