Document Type : Research Paper

Authors

1 Production Engineering and Metallurgy Dept. Baghdad, Iraq,

2 Production Engineering and Metallurgy, University of Technology, Baghdad, Iraq.

Abstract

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation

Keywords

]1[A. D. Evstifeev, G. A. Volkov, A. A. Chevrychkina, and Y. V Petrov, “Strength performance of 1230 aluminum alloy under tension in the quasi-static and dynamic ranges of loading parameters,” Tech. Phys., Vol. 64, No. 5, pp. 620–624, May 2019.
]2[J. Balic, M. Kovacic, and B. Vaupotic, “Intelligent programming of CNC turning operations using genetic algorithm,” J. Intell. Manuf., 2006.
]3[I. R. K. Al-Saedi, F. M. Mohammed, and S. S. Obayes, “CNC machine based on embedded wireless and Internet of Things for workshop development,” 2017 Int. Conf. Control. Autom. Diagnosis, ICCAD 2017, Vol. 4, No. 4, pp. 439–444, 2017.
]4[A. A. D. Laith, and A. Mohammed, “Prediction the effect of cutting parameters on surface roughness using Taguchi method,” Eng. Technology, Vol. 31, No. 17 Part (A) Engineering, pp. 3334–3342, 2013.
]5[H. Öktem, T. Erzurumlu, and M. Çöl, “A study of the Taguchi optimization method for surface roughness in finish milling of mold surfaces,” Int. J. Adv. Manuf. Technology, Vol. 28, No. 7–8, pp. 694–700, 2006.
]6[D. Kanakaraja, A. K. Reddy, M. Adinarayana, L. Vamsi, and K. Reddy, “Optimization of CNC turning process parameters for prediction of surface roughness through Taguchi’S Parametric Design Approach,” Int. J. Mech. Eng. Rob. Res, Vol. 3, No. 4, 2014.
]7[C. Sahay and S. Ghosh, “Understanding surface quality: Beyond average roughness (Ra),” ASEE Annu. Conf. Expo. Conf. Proc., Vol., 2018-June, 2018.
]8[A. F. Ibrahim, M. A. Abdullah, and S. K. Ghazi, “Prediction of surface roughness and material removal rate for 7024 al-alloy in EDM process,” Engineering Tech. Journal, Vol. 34, No. 15, pp. 2796–2804, 2016.
]9[I. Asiltürk and H. Akkuş, “Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method,” Meas. J. Int. Meas. Confed., 2011.
]11[R. J. Crawford, H. K. Webb, V. K. Truong, J. Hasan, and E. P. Ivanova, “Surface topographical factors influencing bacterial attachment,” Advances in Colloid and Interface Science, 2012.
]11[O.P. Imhade and O.C. Ugochukwu , “Effects of Cutting parameters on surface roughness during end milling of aluminium under minimum quantity lubrication (MQL),” Int. J. Sci. Res., Vol. 4, No. 5, pp. 2937–2942, 2015.
]12[J. Gallet, Y. Besse, J. Krystyn, and L. Marcoux, “Norian magneto stratigraphy from the Scheiblkogel section, Austria: constraint on the origin of the Antalya Nappes,” Turkey. Earth Planet. Sci. Lett., Vol. 113–122, p. 140, 1996.
]13[A. Mahamani, “Influence of process parameters on cutting force and surface roughness during turning of aa2219-tib 2 /zrb 2 in-situ metal matrix composites,” Procedia Mater. Sci., Elsevier, Vol. 6, pp. 1178–1186, 2014.