Decision tree-based parametric analysis of a CNC turning process

Document Type : Article


1 Department of Mechanical Engineering, Government Polytechnic, Murtizapur, Maharashtra, India

2 Department of Production Engineering, Jadavpur University, Kolkata, West Bengal, India


Computer numerical control (CNC) is a manufacturing concept where machine tools are automated to perform some predefined functions based on the instructions fed to them. CNC turning processes have found wide ranging applications in modern day manufacturing industries due to their capabilities to produce low cost high quality parts/components with very close dimensional tolerances. In order to exploit the fullest potential of a CNC turning process, it should always be operated while setting its different input parameters at their optimal levels. In this paper, two classification tree algorithms, i.e. classification and regression tree (CART) and Chi-squared automatic interaction detection (CHAID) are applied to study the effects of various turning parameters on the responses and identify the best machining conditions for a CNC process. It is perceived that those settings almost match with the observations of the earlier researchers. The CART algorithm outperforms CHAID with respect to higher overall classification accuracy and lower prediction risk.


1. Trent, E.M., Metal Cutting, Woburn,  assachusetts: Butterworth-Heinemann (2010).
2. Suh, S.-H., Kang, S.K., Chung, D.-H., et al., Theory and Design of CNC Systems, Springer (2008). 
3. Park, K.S. and Kim, S.H. "Artificial intelligent approaches to determination of CNC machining parameters in manufacturing: A review", Artificial Intelligence in Engineering, 12, pp. 127-134 (1997).
4. Gupta, A., Singh, H., and Aggarwal, A. "Taguchifuzzy multi output optimization (MOO) in high speed CNC turning of AISI P-20 tool steel", Expert Systems with Applications, 38, pp. 6822-6828 (2011).
5. Mukherjee, S., Kamal, A., and Kumar, K. "Optimization of material removal rate during turning of SAE 1020 material in CNC lathe using Taguchi technique", Procedia Engineering, 97, pp. 29-35 (2014).
6. Marko, H., Simon, K., Tomaz, I., et al. "Turning parameters optimization using particle swarm optimization", Procedia Engineering, 69, pp. 670-677 (2014).
7. Saini, S.K. and Pradhan, S.K. "Optimization of multiobjective response during CNC turning using Taguchifuzzy application", Procedia Engineering, 97, pp. 141- 149 (2014).
8. Vasudevan, H., Deshpande, N.C., and Rajguru, R.R. "Grey fuzzy multiobjective optimization of process parameters for CNC turning of GFRP/epoxy composites", Procedia Engineering, 97, pp. 85-94 (2014).
9. Saini, S.K. and Pradhan, S.K. "Optimization of machining parameters for CNC turning of different materials", Applied Mechanics and Materials, 592-594, pp. 605-609 (2014).
10. Aghdeab, S.H., Mohammed, L.A., and Ubaid, A.M. "Optimization of CNC turning for aluminum alloy using simulated annealing method", Jordan Journal of Mechanical and Industrial Engineering, 9(1), pp. 39- 44 (2015).
11. Camposeco-Negrete, C. "Optimization of cutting parameters using response surface method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum", Journal of Cleaner Production, 91, pp. 109-117 (2015).
12. Sarkaya, M. and Gullu, A. "Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25", Journal of Cleaner Production, 91, pp. 347-357 (2015).
13. Asilturk, _I., Neseli, S., and _Ince, M.A. "Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods", Measurement, 78, pp. 120-128 (2016).
14. Kumar, U., Singh, A., and Kumar, R. "Optimization of machining parameters for tool wear rate and material removal rate in CNC turning by grey relational analysis", International Journal of Applied Engineering Research, 11(4), pp. 2771-2775 (2016).
15. Maheswara Rao, C. and Venkatasubbaiah, K. "Optimization of surface roughness in CNC turning using Taguchi method and ANOVA", International Journal of Advanced Science and Technology, 93, pp. 1-14 (2016).
16. Klancnik, S., Hrelja, M., Balic, J., et al. "Multiobjective optimization of the turning process using a gravitational search algorithm (GSA) and NSGAII approach", Advances in Production Engineering & Management, 11, pp. 366-376 (2016).
17. Bilga, P.S., Singh, S., and Kumar, R. "Optimization of energy consumption response parameters for turning operation using Taguchi method", Journal of Cleaner Production, 137, pp. 1406-1417 (2016).
18. Kushwaha, A. and Singh, R. "Optimization of CNC  process parameters on Inconel 625 using response surface methodology", International Journal of Mechanical Engineering and Technology, 8(7), pp. 1830- 1836 (2017).
19. Nataraj, M. and Balasubramanian, K. "Parametric optimization of CNC turning process for hybrid metal matrix composite", International Journal Advanced Manufacturing Technology, 93, pp. 215-224 (2017).
20. Nayak, N.K. and Sodhi, H.S. "Optimization of CNC turning parameters for Al-6061 using response surface methodology", International Journal of Mechanical and Production Engineering Research and Development, 7(4), pp. 127-138 (2017).
21. Sahoo, P., Pratap, A., and Bandyopadhyay, A. "Modeling and optimization of surface roughness and tool vibration in CNC turning of Aluminum alloy using hybrid RSM-WPCA methodology", International Journal of Industrial Engineering Computations, 8, pp. 385-398 (2017).
22. Mandal, N.K., Mondal, M., and Singh, N.K. "Modelling and optimisation of a sustainable manufacturing process with CNC turning centre", International Journal of Applied Environmental Sciences, 12, pp. 1101- 1116 (2017).
23. Suresh, M., Meby Selvaraj, R., Rajkumar, K., et al. "Optimisation of cutting parameters in CNC turning of EN-19 using tungsten carbide", International Journal of Computer Aided Engineering and Technology, 9(2), pp. 218-228 (2017).
24. Akkus, H. "Optimising the effect of cutting parameters on the average surface roughness in a turning process with the Taguchi method", Materials and Technology, 52, pp. 781-785 (2018).
25. Bhanu Prakash, P., Brahma Raju, K., Venkata Subbaiah, K., et al. "Application of Taguchi based grey method for multi aspects optimization on CNC turning of AlSi7 Mg", Materials Today: Proceedings, 5, pp. 14292-14301 (2018).
26. Gadekula, R.K., Potta, M., Kamisetty, D., et al. "Investigation on parametric process optimization of HCHCR in CNC turning machine using Taguchi technique", Materials Today: Proceedings, 5, pp. 28446- 28453 (2018).
27. Palanisamy, D. and Senthil, P. "Application of greyfuzzy approach for optimization of CNC turning process", Materials Today: Proceedings, 5, pp. 6645-6654 (2018).
28. Sahoo, P., Satpathy, M.P., Singh, V.K., and Bandyopadhyay, A. "Performance evaluation in CNC turning of AA6063-T6 alloy using WASPAS approach", World Journal of Engineering, 15(6), pp. 700-709 (2018).
29. Saravanakumar, A., Karthikeyan, S.C., Dhamotharan, B., et al. "Optimization of CNC turning parameters on aluminum alloy 6063 using Taguchi robust design", Materials Today: Proceedings, 5, pp. 8290-8298 (2018).
30. Nataraj, M., Balasubramanian, K., and Palanisamy, D. "Optimization of machining parameters for CNC turning of Al/Al2O3 MMC using RSM approach", Materials Today: Proceedings, 5, pp. 14265-14272 (2018).
31. Vasudevan, H., Rajguru, R., Tank, K., et al. "Optimization of multi-performance characteristics in the turning of GFRP (E) composites using principle component analysis combined with grey relational analysis", Materials Today: Proceedings, 5, pp. 5955-5967 (2018).
32. Rao, V.D.P., Mahaboob Ali, S.R.S., Saqheed Ali, S.M.Z.M., et al. "Multi-objective optimization of cutting parameters in CNC turning of stainless steel 304 with TiAlN nano coated tool", Materials Today: Proceedings, 5, pp. 25789-25797 (2018).
33. Vijay Kumar, M., Kiran Kumar, B.J., and Rudresha, N. "Optimization of machining parameters in CNC turning of stainless steel (EN19) by Taguchi's orthogonal array experiments", Materials Today: Proceedings, 5, pp. 11395-11407 (2018).
34. Arun Vikram, K., Lakshmi, V.V.K., and Venkata Praveen, A.M. "Evaluation of process parameters using GRA while machining low machinability material in dry and wet conditions", Materials Today: Proceedings, 5, pp. 25477-25485 (2018).
35. Chau, N.L., Nguyen, M.-Q., Dao, T.-P., et al. "An effective approach of adaptive neuro-fuzzy inference system-integrated teaching learning-based optimization for use in machining optimization of S45C CNC turning", Optimization and Engineering, 20, pp. 811- 832 (2019).
36. Susanti, Y., Zukhronah, E., Pratiwi, H., et al. "Analysis of chi-square automatic interaction detection (CHAID) and classification and regression tree (CRT) for classification of corn production", IOP Conference Series: Journal of Physics, 909, pp. 1-8 (2017).
37. Rokach, L., and Maimin, O., Data Mining with Decision Trees: Theory and Applications, World Scientific Publishing Co., NJ, USA (2014).
38. Han, J., Kamber, M., and Pei, J., Data Mining Concepts and Techniques, Elsevier Inc., USA (2012).
39. Breiman, L., Friedman, J.H., Olshen, R.A., et al., Classification and Regression Tree., Chapman and Hall, New York (1993).
40. Kass, G.V. "An exploratory technique for investigating large quantities of categorical data", Journal of the Royal Statistical Society: Series C (Applied Statistics), 29(2), pp. 119-127 (1980).
41. Pitombo, C.S., de Souza, A.D., and Lindner, A. "Comparing decision tree algorithms to estimate intercity trip distribution", Transportation Research Part C: Emerging Technologies, 77, pp. 16-32 (2017).
42. Sadoyan, H., Zakarian, A., and Mohanty, P. "Data mining algorithm for manufacturing process control", International Journal of Advanced Manufacturing Technology, 28, pp. 342-350 (2006).