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Optimization of turning process parameters by Taguchi-based Six Sigma

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, Six Sigma approach is used for improving the quality process outputs in turning of Galvanized Iron. The objective is to optimize the turning parameters and maximize the MRR (Material Removal Rate). A L16 orthogonal array based on Taguchi experiments consisting of three controlling factors viz. spindle speed, feed rate, and depth of cut, each with four levels as required in traditional DOE setting is used here. Taguchi’s parameter design offers an approach in Design of Experiments (DOE) with control parameters optimization to attain best outcome. An orthogonal array offers a set of balanced least experiments which help in data analysis and prediction of optimum results. For each experiment, the Material Removal Rate (MRR) is calculated. The Taguchi method results in reducing the quality characteristic variation due to uncontrollable parameter through the study of response variation using the Signal to Noise (S/N) ratio by the use of Minitab 16 software. Moreover, statistical investigation shows that standard deviation and mean value of confirmation run data are reduced when compare with before Taguchi design run data was performed.
Rocznik
Strony
649--656
Opis fizyczny
Bibliogr. 10 poz., wykr.
Twórcy
autor
  • Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, 737136, India
autor
  • Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, 737136, India
autor
  • Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, 737136, India
  • Department of Mechanical Engineering, MPSTME Shiprur, SVKM's NMIMS, 425405, India
autor
  • Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, 711103, India
Bibliografia
  • [1] Schwartz, M.: Encyclopedia and Handbook of Materials, Parts and Finishes, CRC Press, 2016.
  • [2] Kwak, Y. H. and Anbari, F. T.: Benefits, obstacles, and future of six sigma approach, Technovation, 26, 5, 708-715, 2006.
  • [3] Roy, R. K.: Design of experiments using the Taguchi approach: 16 steps to product and process improvement, John Wiley & Sons, 2001.
  • [4] Sekulic, M., Kovac, P., Gostimirovic, M. and Kramar, D.: Optimization of high-pressure jet assisted turning process by Taguchi method, Advances in Production Engineering and Management, 8, 1, 5, 2013.
  • [5] Periyanan, P. R., Natarajan, U. and Yang, S. H.: Multi-objective optimization for the micro-end milling process using Taguchi quality loss function, International Journal of Productivity and Quality Management, 10, 4, 484-497, 2012.
  • [6] Vijian, P. and Arunachalam, V. P.: Optimization of squeeze casting process parameters using Taguchi analysis, The International Journal of Advanced Manufacturing Technology, 33, 11-12, 1122-1127, 2007.
  • [7] Vlachogiannis, J. G. and Vlachonis, G. V.: An experimental design for the determination of Cu and Pb in marine sediments using Taguchi’s method, Intern. J. Environ. Anal. Chem., 83, 12, 1021-1034, 2003.
  • [8] Das, M. K., Kumar, K., Barman, T. K. and Sahoo, P.: Optimization of material removal rate in EDM using taguchi method, Advanced Materials Research, 626, 270-274, 2013.
  • [9] Das, M. K., Kumar, K., Barman, T. K. and Sahoo, P.: Optimisation of EDM process parameters using grey-Taguchi technique, International Journal of Machining and Machinability of Materials, 2, 15, 3-4, 235-262, 2014.
  • [10] Roy, A. K. and Kumar, K.: Effect and Optimization of Machine Process Parameters on MRR for EN19 & EN41 materials using Taguchi, Procedia Technology, 14, 204-210, 2014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-92faebd3-7146-494e-8475-97412232cfc2
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