• Shukry H. Aghdeab Production Engineering and Metallurgy Department\University of Technology\Baghdad
  • Baqer Ayad Ahmed
  • Mohammed Sattar Jabbar
  • Asaad Ali Abbas


In this study, the objective is to obtain optimal values of CNC turning parameters (cutting speed, depth of cut and feed rate) which result in an optimal value of surface roughness by machining aluminum shaft. In this work, Taguchi method was carried out on machining of aluminum ENAC-43400 material in dry cutting using CNC turning machine type StarChip 450 equip with carbide cutting tool type DNMG 332. Surface roughness was measured using the POCKET SURF EMD-1500 tester. The results obtained of the surface roughness (Ra) are about (1.14-1.91) μm, and the best was at cutting speed 250 m/min, feed rate 0.05 mm/rev and depth of cut 0.5 mm which is refers to the optimum machining parameters.


[1] Anderson P. Paiva and et al (2007) “A multivariate hybrid approach applied to AISI 52100 hardened steel turning optimization”, Journal of Materials Processing Technology, 189, 26-35.
[2] Anil Gupta and et al (2011) “Taguchi-fuzzy multi output optimization (MOO) in high speed CNC truing of AISI P-20 tool steel”, Expert System with Application, 6822-6828,.
[3] Attanasio and et al, (2011) “Study the effects of tool wear and cutting parameters on white and dark layer formation in hardened AISI 52100 bearing steel”.
[4] Basim A. Khidhir and Bashir Mohamed (2011) “Analyzing the effect of cutting parameters on surface roughness and tool wear when machining nickel based hastelloy-276” IOP Publishing,.
[5] H. K. Dava, L. S. Patel, H. K. Raval, (2012) “Effect of machining conditions on MRR and Surface Roughness during CNC Turning of different Materials Using TiN Coated Cutting Tools- A Taguchi approach”, International Journal of Industrial Engineering Computations 3.
[6] Hussam L. Alwan (2013)" Determination the Optimal Cutting Conditions Affecting the Surface Roughness Using TaguchiI Method in Turning (AL-12%SI) by Carbide Tool" The Iraqi Journal For Mechanical And Material Engineering, Vol.13, No.2.
[7] Ilhan and Ali., (2010) evelopment of a eural etwork ased Surface Roughness Prediction System using utting Parameters and an Accelerometer in Turning , epartment of Mechanical Education, Faculty of Technical Education, University of Seljuk, Turkey, IEEE.
[8] Ilhan Asiltürk and Mehmet Çunkas, (2011) “Modeling and predication of Surface Roughness in truing operations using artificial neural network and multiple regression method”, Expert System with Applications 38, 5826-5832.
[9] Ilhan Asiltürk and Süleyman Neseli, (2012) “Multi response optimization of turning parameters via Taguchi method based response surface analysis”, Measurement Volume 45, Issue 4.
[10] M. Kaladhar and et al (2012) “ etermination of Optimum Process parameters during turning of AISI 304 Austenitic Stainless Steel using Taguchi method and A OVA “International Journal of lean thinking, volume 3, issue 1.
[11] Małgorzata Kowalczyk (2014) "Application of Taguchi and ANOVA Methods in Selection of Process Parameters for Surface Roughness in Precision Turning of Titanium" advanced in manufacturing science and technology,Vol. 38, No. 2.
[12] P.G.Kochure, K.N.Nandurkar (2012) "Taguchi method and ANOVA: An approach for selection of process parameters of induction hardening of EN8 D steel" International Journal of Advance Research in Science, Engineering and Technology, Vol.01, Issue 02, pp. 22 -27.
[13] Rakesh K. Patel and H. R. Prajapati, (2012) , Parametric Analysis of Surface Roughness (SR) and Material Removal Rate (MRR) of arden Steel on Turning using A OVA Analysis , International Journal of Engineering Science and Technology, Vol. 4, No. 07, India.
[14] Rama Rao. S, Padmanabhan. G (2012) " Application of Taguchi methods and ANOVA in optimization of process parameters for metal removal rate in electrochemical machining of Al/5%SiC composites" International Journal of Engineering Research and Applications,vol.2, pp. 192-197.
[15] Raviraj Shetty, R. P. (2008) "Study On Surface Roughness Minimization In Turning of DRACs Using Surface Roughness Methodology And Taguchi Under Pressured Steam Jet Approach" ARPN Journal of Engineering and Applied Sciences, 3 (1), 59-67.
[16] Suleyman Neseli, Suleyman Yaldiz, Erol Turkes, (2011) “Optimization of tool geometry parameters for turning operations based on the response surface methodology”, Measurement 44 580-587.
[17] Tian Syung Lan (2010) “Fuzzy Deduction Material Removal Rate Optimization for Computer umerical ontrol Turning”, American Journal of Applied Sciences, 1026-1031.
How to Cite
H. AGHDEAB, Shukry et al. INFLUENCES AND OPTIMIZATION OF CNC TURNING MACHINING PARAMETERS. Al-Qadisiyah Journal for Engineering Sciences, [S.l.], v. 9, n. 2, p. 200-210, jan. 2018. ISSN 2411-7773. Available at: <>. Date accessed: 21 mar. 2018.

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.