Mathematical Modeling and Optimization of Milling Parameters in AA 5083 Aluminum Alloy

Yıl 2019, Cilt: 3 Sayı: 4, 159 - 163, 20.12.2019

Gökhan Başar Funda Kahraman Ganime Tuğba Önder

https://doi.org/10.26701/ems.537087

Cited By: 3

Öz

In this study, the selection of optimal cutting
parameters for face milling of 5083 aluminum was investigated in order to
minimize the surface roughness. Effect of selected parameters on the surface
roughness was analyzed by using analysis of variance (ANOVA). The mathematical
model was developed to estimate surface roughness in face milling process by
using Response Surface Methodology (RSM). Feed, spindle speed and depth of cut
were selected as input variables. The statistical analysis indicated that feed
and spindle speed have the most considerable influence on surface roughness.
After developed mathematical model, Desirability Function Analysis (DFA) was
performed to optimize the cutting parameters. The lowest value of surface
roughness (0.41 µm) was acquired at a feed of 3008 mm/min, a spindle speed of
5981 rpm and a depth of cut of 0.54 mm.

Anahtar Kelimeler

RSM, ANOVA, DFA, face milling

Kaynakça

• [1] Çetin, M., Bilgin, M., Ulaş, H.B., Tandıroğlu, A. (2011). Kaplamasız sermet takımla AISI 6150 çeliğinin frezelenmesinde kesme parametrelerinin yüzey pürüzlülüğüne etkisi. Electronic Journal of Vocational Colleges, 1(1): 168-176.
• [2] Özel, T., Karpat, Y. (2005). Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools and Manufacture, 45(4-5): 467-479, DOI: 10.1016/j.ijmachtools.2004.09.007.
• [3] Kulekci, M.K., Eşme, U., Ekşi, A.K., Koçoğlu, Z., Yılmaz, N.F. (2017). En Aw 5754 (Almg3) alüminyum alaşımının frezelenmesi işleminde kesme parametrelerinin yüzey pürüzlülüğüne etkisinin incelenmesi. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 32(2): 153-160, DOI: 10.21605/cukurovaummfd.358418.
• [4] Fedai, Y., Ünüvar, A., Akın, H.K., Başar, G. (2019). 316L Paslanmaz çeliklerin frezeleme işlemindeki yüzey pürüzlülüğün ANFIS ile modellenmesi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 7(2): 98-110, DOI: 10.29130/dubited.466629.
• [5] Rubio E.M., Villeta M., Carou D., Saá A. (2014). Comparative analysis of sustainable cooling systems in intermittent turning of magnesium pieces. International journal of precision engineering and manufacturing, 15(5): 929-940, DOI: 10.1007/s12541-014-0419-5.
• [6] Pradhan, S., Singh, G., Bhagi, L.K. (2018). Study on surface roughness in machining of Al/SiCp metal matrix composite using desirability function analysis approach. Materials Today: Proceedings, 5(14): 28108-28116, DOI: 10.1016/j.matpr.2018.10.052.
• [7] Esme, U. (2015). Surface roughness analysis and optimization for the CNC milling process by the desirability function combined with the response surface methodology. Materials Testing, 57(1): 64-71, DOI: 10.3139/120.110679.
• [8] Fnides, M., Yallese, M., Khattabi, R., Mabrouki, T., Girardin, F. (2017). Modeling and optimization of surface roughness and productivity thru RSM in face milling of AISI 1040 steel using coated carbide inserts. International Journal of Industrial Engineering Computations, 8(4): 493-512.
• [9] Palanisamy C., Singh J.S.A., Chinnasamy N. (2017). Development of response surface model to predict the surface roughness during milling of aluminium alloy. International Journal of Science, Engineering and Technology Research, 6(11): 1456-1460.
• [10] Güvercin S., Yıldız A. (2018). Optimization of cutting parameters using the response surface method. Sigma Journal of Engineering and Natural Sciences, 36(1): 113-121.
• [11] Kıvak T. (2014). Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts. Measurement, 50: 19-28, DOI: 10.1016/j.measurement.2013.12.017.
• [12] Vardhan M.V., Sankaraiah G., Yohan M., Rao H.J. (2017). Optimization of parameters in CNC milling of P20 steel using Response Surface methodology and Taguchi Method. Materials Today: Proceedings, 4(8): 9163-9169, DOI: 10.1016/j.matpr.2017.07.273.
• [13] Sarıkaya M., Dilipak H., Gezgin A. (2015). Optimization of process parameters for surface roughness and tool life in face milling using the Taguchi Analysis. Materiali in tehnologije, 49(1): 139–147.
• [14] Basar G., Kirli Akin H., Kahraman F., Fedai Y. (2018). Modeling and optimization of face milling process parameters for AISI 4140 steel. Tehnički glasnik, 12(1): 5-10, DOI: 10.31803/tg-20180201124648.
• [15] Fedai Y., Kahraman F., Kirli Akin H., Basar G. (2018). Optimization of machining parameters in face milling using multi-objective Taguchi technique. Tehnički glasnik, 12(2): 104-108, DOI: 10.31803/tg-20180201125123.
• [16] Gaitonde V.N., Karnik S.R., Maciel C.H.A., Rubio J.C.C., Abrão A.M. (2016). Machinability evaluation in hard milling of AISI D2 steel. Materials Research, 19(2): 360-369, DOI: 10.1590/1980-5373-MR-2015-0263.
• [17] Elkhabeery M.M., Kazamel M.H., Mansour M.M. (2016). Modeling and optimizing of CNC end milling operation utilizing RSM method. International Journal of Advanced Engineering and Global Technology, 4(1): 1612-1618.
• [18] Ariffin S.Z., Razlan A., Ali M.M., Efende, A.M., Rahman M.M. (2018). Optimization of coolant technique conditions for machining A319 aluminium alloy using Response Surface Method (RSM). In IOP Conference Series: Materials Science and Engineering, 319(1): 1-7, DOI: 10.1088/1757-899X/319/1/012039.
• [19] Arjun B., Jayaprakasah R., Kaviyarasu B., Jaganbabu S., Gopalakrishnan, K. (2018). Optimization of cutting parameters in milling of aluminium 7075 alloy using response surface methodology. EPH - International Journal of Science and Engineering, 1(1): 236-243.
• [20] Ekici E., Uzun G., Kıvak T. (2014). Evaluation of the effects of cutting parameters on the surface roughness during the turning of Hadfield Steel with Response Surface Methodology. Uludağ University Journal of The Faculty of Engineering, 19(2): 19-28, DOI: 10.17482/uujfe.38441.
• [21] Pandey R.K., Panda, S.S. (2014). Optimization of bone drilling process with multiple performance characteristics using desirability analysis. APCBEE procedia, 9: 48-53, DOI: 10.1016/j.apcbee.2014.01.009.
• [22] Aggarwal A., Singh H., Kumar P., Singh M. (2008). Optimization of multiple quality characteristics for CNC turning under cryogenic cutting environment using desirability function. Journal of materials processing technology, 205(1-3): 42-50, DOI: 10.1016/j.jmatprotec.2007.11.105.