Experimental Investigation of the Surface Roughness for Aluminum Alloy AA6061 in Milling Operation by Taguchi Method with the ANOVA Technique
Main Article Content
Abstract
The surface roughness of the machined parts is the most important parameter to predict the performance of mechanical components. Moreover, predicting the optimal machining parameters conditions is the preferable method for cost reduction and achieving the desired surface quality of the product. This study investigates three cutting parameters, such as depth of cut, spindle speed, and feed for the milling aluminium alloy AA6061, to predict the surface roughness quality. The experimental work utilized a manual milling machine with a coated carbide cutter. Furthermore, the experiments were arranged using the Taguchi L9 orthogonal array (OA) method. The average surface roughness (Ra) was measured and converted to signal-to-noise (S/N) ratio and then analyzed in the statistical method of analysis of variance (ANOVA). Finally, the optimal combination set speed, feed, and depth of cut was 2400 rpm, 30 mm/min, and 0.5 mm, respectively. Also, according to the ANOVA test, the most influential parameter was the spindle speed among the selected parameters, with the highest P value of (66.42%). In comparison, the lowest P value is a depth of cut (5.34%). Furthermore, spindle speed was the only significant factor statistically. By selecting a high spindle speed (2400 rpm), surface quality was enhanced, but the preferable level was low for depth of cut and feed.
Article received: 10/08/2023
Article accepted: 18/01/2024
Article published: 01/03/2024
Article Details
How to Cite
Publication Dates
Received
Accepted
Published Online First
References
Abdelrazek, A.H., Choudhury, I., Nukman, Y., and Kazi, S., 2020. Metal cutting lubricants and cutting tools: a review on the performance improvement and sustainability assessment. The International Journal of Advanced Manufacturing Technology, 106, pp. 4221-4245. Doi:10.1007/s00170-019-04890-w
Abdulridha, H.H., Helael, A.J., and Al-duroobi, A.A., 2020. Prediction the influence of machining parameters for CNC turning of Aluminum alloy using RSM and ANN. Engineering and Technology Journal, 38(6), pp. 887-895. Doi:10.30684/etj.v38i6A.705
Al Attaby, Q.M.D., Al Saadi, M.H., and Al Naim, I.K.A., 2013. Improvement of AA1050 Sheets. Journal of Engineering, 19(2), pp. 217-234. Doi:10.31026/j.eng.2013.02.05
Altintaş, Y., 1994. Direct adaptive control of end milling process. International Journal of Machine Tools and Manufacture, 34(4), pp. 461-472. Doi:10.1016/0890-6955(94)90078-7
Chinnasamy, M., Rathanasamy, R., Pal, S.K., and Palaniappan, S.K., 2022. Effectiveness of cryogenic treatment on cutting tool inserts: A review. International Journal of Refractory Metals and Hard Materials, 108, 105946. Doi:10.1016/j.ijrmhm.2022.105946
Danesh Narooei, K., and Ramli, R., 2022. Optimal selection of cutting parameters for surface roughness in milling machining of AA6061-T6. International Journal of Engineering, 35(6), pp. 1170-1177. Doi:10.5829/IJE.2022.35.06C.08
Faris, M.S., 2017. Study of the Pitting Corrosion for shot peening 6061-T6 Aluminum Alloy in sea water. Iraqi journal of mechanical and material engineering, 17(4). https://shorturl.at/gqyzW
Fuh, K.H., and Wu, C.F.,1995. A proposed statistical model for surface quality prediction in end-milling of Al alloy. International Journal of Machine Tools and Manufacture, 8(35), pp. 1187-1200. https://shorturl.at/ns149
Ghani, J.A., Choudhury, I., and Hassan, H., 2004. Application of Taguchi method in the optimization of end milling parameters. Journal of materials processing technology, 145(1), pp. 84-92. Doi:10.1016/S0924-0136(03)00865-3
Hussein, S.G., 2014. An experimental study of the effects of coolant fluid on surface roughness in turning operation for brass alloy. Journal of Engineering, 20(3), pp. 96-104. Doi:10.31026/j.eng.2014.03.09
Kadhim, K.J., 2019. Effect of laser process an inclined surface cutting of mild steel then analysis data statistically by RSM. Journal of Engineering, 25(10), pp. 120-133. Doi:10.31026/j.eng.2019.10.09
Kadirgama, K., Noor, M., and Rahman, M., 2012. Optimization of surface roughness in end milling using potential support vector machine. Arabian Journal for Science and Engineering, 37, pp. 2269-2275. Doi:10.1007/s13369-012-0314-2
Karabulut, Ş., 2015. Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method. Measurement, 66, pp. 139-149. Doi:10.1016/j.measurement.2015.01.027
Khan, A.A., and Kaiser, M.S., 2022. Wear studies on Al-Si automotive alloy under dry, fresh and used engine oil sliding environments. Research on Engineering Structures & Materials, 9(1), pp. 1-18. Doi:10.17515/resm2022.505ma0816
Khorasani, A.M., Yazdi, M.R.S., and Safizadeh, M.S., 2012. Analysis of machining parameters effects on surface roughness: a review. International Journal of Computational Materials Science and Surface Engineering, 5(1), pp. 68-84. Doi:10.1504/IJCMSSE.2012.049055
Khudair, A.M., and Hussein, F.I., 2021. Parametric optimization for fatigue life of 6061-t6 Aluminum thin sheets processed with high-speed laser shock peening. Iraqi Journal of Laser, 20(2), pp. 8-17. Doi:10.31900/ijl.v20i2.281
Kumar, S., Maity, S.R., and Patnaik, L., 2019. Box-Behnken analysis of surface modification of Aluminium Alloy AA6061 using roller burnishing. Materials Today: Proceedings, 18, pp. 4613-4621. Doi:10.1016/j.matpr.2019.07.445
Lee, T., and Lin, Y., 2000. A 3D predictive cutting-force model for end milling of parts having sculptured surfaces. The International Journal of Advanced Manufacturing Technology, 16(11), pp. 773-783. Doi:10.1007/s001700070011
Mukherjee, I., and Ray, P.K., 2006. A review of optimization techniques in metal cutting processes. Computers and Industrial Engineering, 50(1), pp. 15-34. Doi:10.1016/j.cie.2005.10.001
Najiha, M., Rahman, M., and Kadirgama, K., 2016. Performance of water-based TiO2 nanofluid during the minimum quantity lubrication machining of Aluminium alloy, AA6061-T6. Journal of cleaner production, 135, pp. 1623-1636. Doi:10.1016/j.jclepro.2015.12.015
Nouveau, C., Jorand, E., Decès-Petit, C., Labidi, C., and Djouadi, M.A., 2005. Influence of carbide substrates on tribological properties of chromium and chromium nitride coatings: application to wood machining. Wear, 258(1-4), pp. 157-165. Doi:10.1016/j.wear.2004.09.034
Ostapenko, M., and Vasilega, D., 2013. Method of evaluation of quality of metal-cutting tool. Applied Mechanics and Materials, 379, pp. 49-55. Doi:10.4028/www.scientific.net/AMM.379.49
Öztürk, B., and Kara, F., 2020. Calculation and estimation of surface roughness and energy consumption in milling of 6061 alloy. Advances in Materials Science and Engineering, pp. 1-12. Doi:10.1155/2020/5687951
Pan, J., Ni, J., He, L., Cui, Z., and Feng, K., 2020. Influence of micro-structured milling cutter on the milling load and surface roughness of 6061 aluminum alloy. The International Journal of Advanced Manufacturing Technology, 110(11), pp. 3201-3208. Doi:10.1007/s00170-020-06080-5
Patel, N., Parihar, P. L., and Makwana, J. S., 2021. Parametric optimization to improve the machining process by using Taguchi method: a review. Materials Today: Proceedings, 47, pp. 2709-2714. Doi:10.1016/j.matpr.2021.03.005
Raju, K.V.M.K., Janardhana, G.R., Kumar, P.N., and Rao, V.D.P., 2011. Optimization of cutting conditions for surface roughness in CNC end milling. International journal of precision engineering and manufacturing, 12, pp. 383-391. Doi:10.1007/s12541-011-0050-7
Rao, R.S., Kumar, C.G., Prakasham, R.S., and Hobbs, P.J., 2008. The Taguchi methodology as a statistical tool for biotechnological applications: a critical appraisal. Biotechnology Journal: Healthcare Nutrition Technology, 3(4), pp. 510-523. Doi:10.1002/biot.200700201
Ribeiro, J., Lopes, H., Queijo, L., and Figueiredo, D., 2017. Optimization of cutting parameters to minimize the surface roughness in the end milling process using the Taguchi method. Periodica Polytechnica Mechanical Engineering, 61(1), pp. 30-35. Doi:10.3311/PPme.9114
Rosa, J.L., Robin, A., Silva, M., Baldan, C.A., and Peres, M.P., 2009. Electrodeposition of copper on titanium wires: Taguchi experimental design approach. Journal of materials processing technology, 209(3), pp. 1181-1188. Doi:10.1016/j.jmatprotec.2008.03.021
Sabree Bedan, A., Hassan Shabeeb, A., and Nemaha Al-Sobyhawe, H., 2016. Modeling and optimization of machine parameters using Simulated Annealing Algorithm (SAA). Engineering and Technology Journal, 34(7), pp. 1473-1482.
Salman, K.D., 2017. Comparison the physical and mechanical properties of composite materials (Al/SiC and Al/B4C) produced by powder technology. Journal of Engineering, 23(10), pp. 85-96. Doi:10.31026/j.eng.2017.10.07
Sandvik, 2023. Sandvik Coromat. shorturl.at/ptx79
Selden, P.H., 1996. Sales process engineering: a personal workshop. ASQ Quality Press.
Shukla, A., Kotwani, A.s, and Unune, D.R., 2020. Performance comparison of dry, flood and vegetable oil based minimum quantity lubrication environments during CNC milling of Aluminium 6061. Materials Today: Proceedings, 21, pp. 1483-1488.
Tsai, Y.H., Chen, J.C., and Lou, S.J.,1999. An in-process surface recognition system based on neural networks in end milling cutting operations. International Journal of Machine Tools and Manufacture, 39(4), pp. 583-605. Doi:10.1016/S0890-6955(98)00053-4