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Analysis and Optimization of End Milling Machining Parameters for Polypropylene composite using Taguchi based GRA

Gajendra Arya, M. K. Gaur, Saurabh Agrawal

Abstract


This paper offerings the effect of machining parameters on Surface roughness (Ra & Rz) and Machining time during end milling of polypropylene material on CNC milling machine at different levels. In this effort, the machining parameters namely milling speed (MS), feed (Feed) and depth of cut (DOC) were designed using Taguchi L16 experimental design matrix. The impact of all the input parameters on the response parameters (surface roughness and machining time) are optimized using GRA (grey relational analysis). The result of established mathematical model was scrutinized by ANOVA. Investigational results indicate that the feed rate (FD) is the most significant factor with the percentage contribution of 57.89% which affect the Surface roughness and Machining time during the process of material. The effect of milling speed and depth of cut are found to be insignificant as compared to the feed rate and their percentage contribution is 8.97% and 14.85%. The feed rate is suggested to be kept at the lower level for better optimization results. Finally, the confirmation test is carried out to measure the effectiveness of applied methodology.


Keywords


Polypropylene; End milling; Machining parameters; Taguchi design; GRA; ANOVA

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References


Agrawal, S., Gaur, M. K., Kasdekar, D. K., Agrawal, S., & Malvi, C. S. (2015). Optimal Machining Condition for Turning of Hard Porcelain using Response Surface Methodology. European Journal of Advances in Engineering and Technology, 2(5), 44-51.

Asiltürk, I., & Neşeli, S. (2012). Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis.Measurement, 45(4), 785-794.

Jayaraman, P. (2014). Multi-response Optimization of Machining Parameters of Turning AA6063 T6 Aluminium Alloy using Grey Relational Analysis in Taguchi Method. Procedia Engineering, 97, 197-204.

Kasdekar, D.K., Pareshar, V. (2014). Study on Responses Parameter of EDM Process in EN-353 Steel Using Grey Relational Analysis. Journal of Mechatronics and Automation, 1(3).

Khan, Z. A., Siddiquee, A. N., Khan, N. Z., Khan, U., & Quadir, G. A. (2014). Multi response optimization of wire electrical discharge machining process parameters using Taguchi based grey relational analysis. Procedia Materials Science, 6, 1683-1695.

Korkut, I., Kasap, M., Ciftci, I., & Seker, U. (2004). Determination of optimum cutting parameters during machining of AISI 304 austenitic stainless steel.Materials & Design, 25(4), 303-305.

Kuram, E., & Ozcelik, B. (2013). Multi-objective optimization using Taguchi based grey relational analysis for micro-milling of Al 7075 material with ball nose end mill. Measurement, 46(6), 1849-1864.

Lin, C. L., Lin, J. L., & Ko, T. C. (2002). Optimisation of the EDM process based on the orthogonal array with fuzzy logic and grey relational analysis method. The International Journal of Advanced Manufacturing Technology,19(4), 271-277.

Lin, J. L., & Lin, C. L. (2002). The use of the orthogonal array with grey relational analysis to optimize the electrical discharge machining process with multiple performance characteristics. International Journal of Machine Tools and Manufacture, 42(2), 237-244.

Mahapatra S.S., Patnaik A., Patnaik P., 2006. parametric analysis and optimization of cutting parameters for turning operations based on Taguchi method, in: Proceedings of the Int. Conference on Global Manufacturing and Innovation, pp. 1–6.

Maiyar, L. M., Ramanujam, R., Venkatesan, K., & Jerald, J. (2013). Optimization of machining parameters for end milling of Inconel 718 super alloy using Taguchi based grey relational analysis. Procedia Engineering, 64, 1276-1282.

Nayak, S. K., Patro, J. K., Dewangan, S., & Gangopadhyay, S. (2014). Multi-Objective Optimization of Machining Parameters During Dry Turning of AISI 304 Austenitic Stainless Steel Using Grey Relational Analysis. Procedia Materials Science, 6, 701-708.

Oliveira, J. F. G., Silva, E. J., Guo, C., & Hashimoto, F. (2009). Industrial challenges in grinding. CIRP Annals-Manufacturing Technology, 58(2), 663-680.

Parida, A., Bhuyan, R., & Routara, B. (2014). Multiple characteristics optimization in machining of GFRP composites using Grey relational analysis.International Journal of Industrial Engineering Computations, 5(4), 511-520.

Ross P.J., 1996.Taguchi Techniques for Quality Engineering, McGraw-Hill Book Company, New York.

Sadasiva Rao, T., Rajesh, V., & Venu Gopal, A. (2012, March). Taguchi based grey relational analysis to optimize face milling process with multiple performance characteristics. In International Conference on Trends in Industrial and Mechanical Engineering (ICTIME'2012) (Vol. 24, p. 25).

Sahoo, A. K., Baral, A. N., Rout, A. K., & Routra, B. C. (2012). Multi-objective optimization and predictive modeling of surface roughness and material removal rate in turning using grey relational and regression analysis. Procedia Engineering, 38, 1606-1627.

Sarıkaya, M., & Güllü, A. (2015). 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, 347-357.

Tekıner, Z., & Yeşılyurt, S. (2004). Investigation of the cutting parameters depending on process sound during turning of AISI 304 austenitic stainless steel. Materials & Design, 25(6), 507-513.

Tosun, N., & Pihtili, H. (2010). Gray relational analysis of performance characteristics in MQL milling of 7075 Al alloy. The International Journal of Advanced Manufacturing Technology, 46(5-8), 509-515.

Wang, Z., Meng, H., & Fu, J. (2010). Novel method for evaluating surface roughness by grey dynamic filtering. Measurement, 43(1), 78-82.

Xavior, M. A., & Adithan, M. (2009). Determining the influence of cutting fluids on tool wear and surface roughness during turning of AISI 304 austenitic stainless steel. Journal of materials processing technology, 209(2), 900-909.




DOI: https://doi.org/10.37591/tmet.v8i3.903

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