(Microsoft Word - \321\324\307 \321\307\343\322123- 128) Al-Khwarizmi Engineering Journal Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, September, (2018) P.P. 123- 128 Investigation of CNC Milling Machining Parameters on Surface Roughness Rasha Ramiz Alyas Department of Production Engineering and Metallurgy / University of Technology/ Baghdad/ Iraq Safaa_kadhim1988@yahoo.comEmail: (Received 20 June 2017; accepted 13 January 2018) https://doi.org/10.22153/kej.2018.01.008 Abstract Milling Machining is a widely accepted nontraditional machining technique used to produce parts with complex shapes and configurations. The material is removed in two stages roughing and finishing, the flat end cutter removed the unwanted part of material, then finished by end mill cutter. In milling technique, the role of machining factors such as cutting depth, spindle speed and feed has been studied using Taguchi technique to find its effectiveness on surface roughness. Practical procedure is done by Taguchi Standard matrix. CNC milling is the most conventional process which is used for removing of material from workpiece to perform the needed shapes. The results and relations indicate that the rate of feed is very important factor for modeling surface roughness. The plot of S/N ratio shows that the optimum combination of the milling factors that gives the best value of surface accuracy. The best combination of milling factors has also been predicted to minimize the surface roughness. Keywords: Orthogonal array, surface roughness, S/N ratio, Taguchi technique. 1. Introduction Producing components with complex surfaces don’t like producing parts with regular surfaces. This is because the big difference between complex and regular surfaces, or the freedom in designing of the complex surfaces. Now days the technique of CNC machines has been widely used in various manufacturing fields to meet the advance requirements, especially in accurate metal forming industry. Furthermore, geometric features made of complex surfaces are often produced in combination, whilst those with regular shapes are normally machined individually [1, 2]. The requirements for high performance and automated machining systems interested with the surface conditions of the part, product roughness of the machined parts is most important because of the effect on part shape, function, and product life. It is the most general used in a wide number of manufacturing processes. Milling is the popular widely used metal cutting process [3]. This paper reveal the role of some of machining factors like (feed rate, depth of cut and spindle speed) on surface roughness in CNC milling process. For these causes it is an important to keep the surface finish and consistent clearances. Between many CNC production machining techniques, milling process is a good machining technique. An important role in performance of machining, the nature of the surface plays as a best- accuracy machined surface significantly improves fatigue strength, creep life and corrosion resistance. The resulted surface from milling is affected by many parameters like temperature, vibration, rotational speed, tool shape, feed, tool path, cross-feed and other factors [4]. Machining parameter specified range plays an important action on surface quality. In milling, use of low depth of cut, high spindle speed and low feed rate are recommended to find out best surface roughness for the specified range in a material. As the applied force increased Surface roughness is increased. If we used a small Rasha Ramiz Alyas Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, P.P. 123- 128 (2018) 124 nose radius at constant feed cause the higher surface roughness, with the modern production requirements of modern industries; the control of surface roughness with the surface accuracy became more important matter [5,6]. Cutting speed is the most important parameters that effect on surface roughness. The most important combinations that affect product roughness are between the depth of cut and cutting speed, and between spindle speed and feed rate. This practical investigation demonstrates the Taguchi optimization technique, which is applied to find the best surface roughness in end milling operation [7]. Aluminum alloy was the workpiece and the experiment is conducted on it by (HSS) tool with finishing pass. The machining conditions such as feed rate, rotational speed and cutting depth. Taguchi L9 orthogonal array was used to conduct the experiments. 2. Experiment and Data Collection The experiments were designed using L9 Taguchi orthogonal array. Minitab16 was the technique that used for arrangement of experiment. Working within the Minitab16 program is done in three stages. These stages are firstly the planning stage, secondly the conducting stage, finally the analyzing stage. A) Planning Stage In the planning stage, the cutting factors are set according to their level and according to the table (1) which includes the cutting factors (Rotational speed, feed, cutting depth). Table 1, Selected input Parameter Factor A ( rpm) Factor B (mm/min) Factor C (mm ) 1000 100 1 1250 150 1.5 1500 200 2 B) Experiments of Design Taguchi L9 orthogonal array was used for selected input parameters experiments design as shown table (2). For this purpose, Minitab software 16 was used: Table 2, Experiments design using Minitab software. Exp. no Rotational speed- (RPM) (A) Feed - (mm/min) (B) Cutting depth- (mm) (C) 1 1000 100 1 2 1000 150 1.5 3 1000 200 2 4 1250 100 1 5 1250 150 2 6 1250 200 2 7 1500 100 2 8 1500 150 1 9 1500 200 1.5 C) Workpiece Material The workpiece that used in the present paper is aluminum alloy with dimensions (50 x 50 x 25 mm). whose compositions are as follows: Si 1.1% Cu 4.5% Rest Al And has mechanical properties as follows: Tensile Strength = 221 (MPa) Yield Strength = 110 (MPa) Ductility = 8.5 (%Elongation in 50 mm) Applications Crankcases, Flywheel, Bus, axle housing and aircraft parts. D. Experiments Set Up The completion of the nine samples on the CNC milling machine the surface roughness values are taken and by determining the least readability of the roughness, which is the best value, the best factors are determined through the prediction by the program Minitab. The value of S/N ratio (Means that the amount of the output signal (resulting from the process) to the error rate and the greater the value, the better the reading of the prediction) of each experiment was calculated based on equation no.1. Fig 1. CNC Machine. Rasha Ramiz Alyas Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, P.P. 123- 128 (2018) 125 The equation is SNi = 10ـــ x log ( � � ∑ ���� y 2 i ) ... (1) Where n = number of measurements in a trial/row, in this case, n=3, SNi is the signal to noise ratio of ith term, and Yi is the ith value of measured in a run/row [8]. 3. Apparatus Used to Calculate Roughness Measurements The important output properties that can be measured is surface roughness which measured in the present paper by using Pocket Surf the portable surface roughness gage Mahr Federal’s patented that shown in figure (2) below. The Mahr Federal’s is a surface-roughness measuring apparatus, which moves on the surface of machined parts to measure the surface roughness depending on standards, later the results displayed on LCD display. In addition, a reading of Ra was taken, which gives a passage on the surface and gives a rate of surface roughness as shown in table (3), which is considered the most widely used industry. The stylus moves a specified displacement during the movement the data processed and appear on a gage liquid crystal display. Fig 2. Pocket Surf tester Table 3, Surface roughness value for each Experiments. No Rotational speed (rpm) (A) Feed (mm/min) (B) Cutting depth (mm) (C) Surface roughness (R1) (μm) R2 R3 S/N Ratios (dbi) MEANS 1 1000 100 1 1.781 1.975 1.927 5.5547- 1.89433 2 1000 150 1.5 2.340 2.482 2.359 7.5843- 2.39367 3 1000 200 2 2.730 2.292 2.482 7.9857- 2.50133 4 1250 100 1 1.001 1.299 1.273 1.5735- 1.19100 5 1250 150 2 1.027 1.839 1.405 3.2978- 1.42367 6 1250 200 2 1.157 1.283 1.284 1.8878- 1.24133 7 1500 100 2 0.455 0.628 0.691 4.4414 0.59133 8 1500 150 1 0.676 0.761 0.934 1.9644 0.79033 9 1500 200 1.5 4.017 4.126 4.938 12.8288- 4.36033 Rasha Ramiz Alyas Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, P.P. 123- 128 (2018) 126 Fig 3. S/N ratio plot Fig. 4. Means plot. Figure (3) shows the relationship between input parameters and S/N ratio data. While Figure (4) In this diagram, the relationship between the surface roughness value rate and each of the cutting factors is indicated with their levels and by the lowest roughness value that gives the value of the factor to the best reading. The aim of using S/N ratio as a measurement of performance is to develop a process in sensitive to noise factor. The best input parameters can be found of combination from plot (3) i.e. plote between input parameters and S/N ratio, the optimum setting of process parameters are (A2B1C1). The selected parameters with the highest S/N ratio generally yields the optimum quality with minimum variation. 4. Calculation of The Performance The effect of all factors on surface roughness is found according to the following equation [9]: Average performance of factor A at level 1= �� � � � ����� �� ��� � ������ ... (2) According to the Minitab program, the most effective factor is calculated on the cutting process known as Rank and according to Table (4). The following table shows the most effective factor is the cutting depth (Rank 1) and the lowest factor is the cutting speed (Rank 3). Table 4, Table for Signal / Noise Ratio Minimum of S/N ratio is better Level of parameter A B C 1 -7.0424 -0.8965 -1.7222 2 -2.2530 -2.9726 -10.2065 3 -2.1410 -7.5674 -2.1825 Delta 4.9015 6.6710 8.4844 Rank 3 2 1 5. Calculation of Optimal Surface Roughness Let R = surface roughness average result for 9 runs R � ∑ ��������� � � ! " = 1.8208 6. Result and Discussion Using these data the best surface roughness can be predicted using the optimum machining conditions mentioned above and according to the relation: Predicted Mean (Ra) = A2 +B1 +C1– 2(average mean) ...(3) Where A2= Average of (Ra) at the second level of spindle speed B1= Average of (Ra) at the first level of feed rate C1= Average of (Ra) at the first level of depth of cut From Table (7): Predicted Mean (Ra) = 1.285+1.226+1.292- 2*1.8208= 0.1614 µ m 7. Conclusions 1. The plot of S/N ratio show that the optimum value of surface quality is found at first value of factor B, the second value of factor A and first value of factor C. Rasha Ramiz Alyas Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, P.P. 123- 128 (2018) 127 2. From the results it can be seen that the Optimum value of surface roughness is 0. 0.1614 μm. 3. Taguchi technique is the best way to determine the number of samples by introducing the effects with their levels to determine the minimum number that can be run through its matrices. The Taguchi method is widely used and found to be suitable for determining the cutting effects of the programmed milling machine. 8. References [1] Dalgobind Mahto and Anjani Kumar,” Optimization of Process Parameters in Vertical CNC Mill Machines Using Taguchi’s Design of Experiments”, Ariser Vol. 4 No. 2 (61-75,2008 [2] Tao Ye, Cai-Hua Xiong,” Geometric Parameter Optimization in Multi-Axis Machining”, Computer- Aided Design 40 (2008) 879–890,2008 [3] S. Doruk Merdol, Yusuf Alt,” Virtual Cutting and Optimization of Three-Axis Milling Processes”, International Journal of Machine Tools & Manufacture 48 1063– 1071,2008 [4] H.-S. Lu, J.-Y. Chen, Ch.-T. Chung,” The Optimal Cutting Parameter Design of Rough Cutting Process in Side Milling”, Volume 29 Issue 2, 2008 [5] M.A. Lajis, A.N. Mustafizul Karim, A.K.M. Nurul Amin, A.M.K. Hafiz, L.G. Turnad,” Prediction of Tool Life in End Milling of Hardened Steel Aisi D2”, Issn 1450-216x Vol.21 No.4, Pp.592-602,2008 [6] J. V. Abellan, F. Romero, H. R. Siller, A. Estruch and C. Vila,” Adaptive Control Optimization of Cutting Parameters for High Quality Machining Operations Based On Neural Networks and Search Algorithms”, Isbn 78-953-7619-16-9, Pp. 472, I-Tech, Vienna, Austria,2008 [7] Guillem Quintana, Joaquim Ciurana , Daniel Teixidor,” A New Experimental Methodology For Identification Of Stability Lobes Diagram In Milling Operations “,International Journal Of Machine Tools & Manufacture 48 (2008) 1637–1645,2008 [8] M.Kurt & E.Bagci & Y.Kaynak, “Application of Taguchi Methods in the Optimization of Cutting Parameters for Surface Finish and Hole Diameter Accuracy in Dry Drilling Processes”, Int. J Adv. Manufacturing Technology -1368-2, (2007). [9] M.T. Hayajneh, M.S. Tahat, J. Bluhm, “Study of The Effects of Machining Parameters On the Surface Roughness in The End-Milling Process”, Jordan Journal of Mechanical and Industrial Engineering, (2007). )2018( 123-128، صفحة 3د، العد14دجلة الخوارزمي الهندسية المجلم رشا رامز الياس 128 تأثير متغيرات التشغيل في مكائن التفريز المبرمجة على الخشونة السطحية الياسرشا رامز قسم هندسة اإلنتاج والمعادن / الجامعة التكنولوجية yahoo.com1988Safaa_kadhim@ البريد االلكتروني: الخالصة واالنهاء القطع الخشن ازالة المادة تتم على مرحلتين. معقدة أجزاء ألشكال إلنتاج المستخدمة للقطع التقليدي مقبولة أكثر تقنية هو مكائن التفريز في االنهاء السطحي يتم استخدام عدة قطع ذو نهاية ثم العينة، من المطلوب الجزء مسطحة وإزالة القطع الخشن يتم استخدام عدة قطع ذونهاية السطحي، . السطحية للخشونة قيم افضل إليجاد تاكوشي تقنية باستخدام والتغذية القطع ةوسرع القطع عمق مثل التشغيل عوامل تأثير دراسة تم هذا البحث، في. مستديرة المشغولة من المعدن إلزالة تستخدم التي تقليدية األكثر العمليات هو التفريز بالمكائن المبرمجة. القياسية تاكوشي مصفوفة قبل من العملي تحديد الجزء يتم االشارة للخشونة الى معدل نسبة وتظهر. السطح خشونة للتأثير على جدا مهم عامل هو التغذية معدل أن إلى تشير والعالقات النتائج. المطلوبة األشكال النتاج .سطحية خشونة افضل التي تعطي عوامل أفضل توقع تم كما. السطح لدقة قيمة أفضل تعطي التي القطع عوامل بين األمثل تعطيالخطأ