Full Paper Proc. of Int. Conf. on Advances in Mechanical Engineering 2012

Experimental Analysis of CNC Turning using Taguchi Method Sujit Kumar Jha1, Sujit Singh2 and Shiv Ranjan Kumar3 1

Engineering Department, Ibra College of Technology, Ibra, Sultanate of Oman skj828@gmail.com 2 Department of Mech. Engg., Jaypee University of Engineering & Technology, Raghogarh, India singhsujit@hotmail.com 3 Department of Mechanical Engineering, Sir Padampat Singhania University, Udaipur, India shiv.ranjan@gmail.com Taguchi of Nippon Telephones and Telegraph Company Japan for the production of robust products. According to Taguchi, quality of a manufactured product has total loss generated by that product to society from the time has shipped. Taguchi developed a method based on or thogonal ar r a y experiments, which reduced “variance” for the experiment with “optimum settings” of control parameters. Thus the combination of Design of Experiments (DOE) with optimization of control parameters to obtain best results have achieved in the Taguchi Method. Signal to noise (S/N), ratio and orthogonal array are two major tools used in robust design. Signal to noise ratio, which are log functions of desired output measures quality with emphasis on variation, and orthogonal arrays, provide a set of well balanced experiments t o accommodates many design factors simultaneously [5, 6]. Taguchi’s robust design method is suitable to analyze the metal cutting problem by considering the optimization in end milling using S/N ratio and ANOVA method. Ghani et.al [7] established that the conceptual S/N ratio and ANOVA approaches for data analysis in end milling uses at high cutting speed of 355 m/min, low feed rate of 0.1mm per tooth and low depth of cut of 0.5 mm. Application of Taguchi’s method for parametric design has been carried out to determine an ideal feed rate and desired force combination, the experimental results showed that surface roughness decreases with a slower feed rate and larger grinding force, respectively [8]. Feng. Cang-Xue (Jack) [9] considered the impact of turning parameters on surface roughness of work material depends on material, nose radius of tool, feed, speed and depth of cut. He has investigated that the feed has the most significant impact on the observed surface roughness. Jafar Zare and Afsari Ahmad [10] mentioned the performance characteristics in turning operations of Df2 (1.2510) steel bars using TiN coated tools. Three cutting parameters namely, cutting speed, feed rate, and depth of cut, will be optimized with considerations of surface roughness. Pramod S (2006) [11] demonstrated a systematic procedure of using Taguchi technique for optimising MRR in Electric Discharge Machine (EDM). S. Thamizhmanii (2007) [12] applied Taguchi methodology to optimize cutting parameters in CNC turning for surface roughness. In section 2 Methodology of the experiment has been described.

Abstract – The objective of this research is to utilize Taguchi methods to optimize material removal rate (MRR) during machining operation on CNC turning of aluminium samples. The material removal rate has been identified as the quality traits and assumed to be directly linked to productivity. There are three important cutting parameters namely, cutting speed, feed rates and depth of cut, which has been considered during the turning operation. An Orthogonal array has been constructed to determined the signal-to-noise (S/N) ratio. According to experimental results, depth of cut is determined to be the most important factor on material removal rate during turning operations. Ind ex terms - Tag uchi method, CNC Turning, Cutting Parameters, MRR.

I. INTRODUCTION The high speed machining (HSM) and modern machining technologies has been used to machine the parts that need significant amount of material removal. Turning is one of the most important machining process in which a single point cutting tool removes unwanted material from the surface of a rotating cylindrical work piece [1]. The process parameters like cutting speed, feed rate, depth of cut, coolant condition and tool geometry affects the material removal rate in turning. The proper selection of process parameters is essential to optimize the metal removal rate. The objective of this paper is to investigate process parameters for a turning aluminium work piece on EMCO CNC turning machine. In this study, three levels of speed, feed and depth of cut are evaluated for high material removal rate (MRR). There are many cutting parameters like cutting speed, feed rate and depth of cut has been selected to optimize the economics of machining operations, as assessed by productivity, total manufacturing cost per part or some other criterion. Regardless of the early works on setting up optimum cutting speeds in Computerized Numerical Controlled (CNC) machining, the recent research [2, 3, 4] have mentioned that the process parameters need to be optimized during CNC machining is an essential and costly process for small and medium type manufacturing industries. The common tendency of process is to reduce the machining cost and time and increasing the accuracy of the product. Taguchi method is statistical method developed by Professor Genichi © 2012 AMAE DOI: 02.AETAME.2012.3.15

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Full Paper Proc. of Int. Conf. on Advances in Mechanical Engineering 2012 Taguchi Method has been presented in section 3. Material Study for this research is described in section 4. In section 5, Experimental Method has been described and Section 6, presented Results and Discussion. Finally, section 7 presents conclusions.

vibration, friction, etc for the deviation of the output from the desired output. Response is the outcome of the Product/ Process after giving three input variables. The figure 1 shows the various influencing factors of product/process design

II. METHODOLOGY There are various methodologies by which CNC machining operation can be optimized to improve the quality of a product or process. The “Build-test-fix” is the primal approach to conduct the process according to the resources available, rather than optimize it. On the other hand, the objective of “One-factor-at-a-time” approach is to optimize the process by running an experiment at one particular condition and repeating the same experiment by changing one factor till the effect of all the factors are known. A. Design of Experiments The Design of Experiments (DOE) is the most powerful statistical technique in product\process development. The general quantitative approach which is more logical has been selected for designing the experiments to achieve a predictive knowledge of a complex, multi-variable process with the fewest trials possible. In this research, a 3 factor three level factorial technique has been employed for the development of design matrix to conduct the experiments. A full factorial design may also be called a fully crossed design. If there are k factors, each at 2 levels; a full factorial design has 2k runs. III. TAGUCHI METHOD In Taguchi method, the main parameters have influence on process results, which are positioned at different rows in a designed orthogonal array. The difference between the functional value and objective value is recognized as the loss function that can be expressed by signal-to-noise (S/N) ratio. The category the larger-to-the-better was used to calculate S/N ratio for material removal rate, according to the equation:

1 n 1 S / N 10log10 2 r i 1 yi

Fig. 1. Product/process diagram

IV. MATERIAL STUDY Pure aluminum is soft, ductile, and corrosion resistant with high electrical conductivity. It has strength to weight ratio superior to steel. Common alloying elements are copper, zinc, magnesium, silicon, manganese and lithium to improve strength and strain hardening ability of aluminum. Traditional machining operations like turning, milling, boring, tapping, etc are easily performed on aluminium and its alloys. However, some machining parameters such as rotational speed should be less than steel and feed rates are lower on thin walled surface. Normally, higher speeds, feeds and depth of cut may be employed in many applications depending on the nature of the parts, machine tool, tool design, lubrication and other cutting conditions. There are four parameters like cutting force, tool life, surface quality, and chip formation have impact on machinability of a material [14]. V. EXPERIMENTAL METHOD The details of experiments, experimental variables and constants have been shown in the Table I. T ABLE I. EXPERIMENTAL DETAILS

Traditional experimental design methods are very complicated and difficult to implement, as it require a large number of experiments by increasing the process parameters [13]. To minimize the number of tests, Taguchi developed a particular design of orthogonal arrays to study the entire parameter space with small number of experiments. A. Product / Process Diagram A Product / Process diagram is used to designate the different factors influences of a product/process. The signal factor, like product design or sequence of process is the input into the Product/Process design. The control factors are the factors, which can be controlled to obtain the desired output, like speed, cutter radius, etc. The noise factors are the uncontrollable factors, like temperature, humidity, 93 © 2012 AMAE DOI: 02.AETAME.2012.3.15

In this paper, there are three cutting parameters: cutting speed, feed rate and depth of cut has been considered for three levels. Three variables are studied for three levels and hence nine experiments were designed and conducted based on Taguchi’s L9 orthogonal array.

Full Paper Proc. of Int. Conf. on Advances in Mechanical Engineering 2012 The experiments were conducted on EMCO CNC turning machine, which is highly versatile with the latest CNC technology, driven by latest CNC control system.Figure 2 the configuration of the machine as listed. Industrial design: 2 axes slant bed lathe, Tool Turret: 12 station

VDI automatic tool changer optional 6 driven tool, Max turning diameter: 85 mm, Distance between centre 405 mm, Travel X Z: 100*250 mm, Spindle speed: 60 – 6300 rpm, Max bar stock diameter: 25.5 mm, Feed force: 0-3 N and Display: 12" LCD.

Fig. 2. EMCO Concept Turn 250

A. Identifying Control Factors

B. Experimental Plan and Experimentation

Cutting speed, depth of cut and feed rate are considered as the control factors. The cutting parameters and their levels of this experiment are shown in Table II.

The component has drawn in AutoCAD 11 and shown in the Figure 3. Experiments have been designed and carried out using Taguchi’s L9 Orthogonal Array (OA). The objective of this research to obtain a mathematical model that relates the material removal rate to three cutting parameters in CNC turning process. In this research, a 3 factor three level factorial technique has been implemented for the development of design matrix to conduct the experiments and construction of the orthogonal array for this experiment.

TABLE II. C UTTING PARAMETERS AND T HEIR LEVELS

C. Sample Calculation of MRR and S/N ratio The turning operations are carried out as per the L9 Orthogonal Array and weight of the work piece before machining and after machining is measured and tabulated in the Table 4. Cycle time required for machining for each experiment is observed. Metal removal rate is calculated using the formula: Metal Removal Rate in gms/Minute = Weight of Metal Removed in grams Divided by Cycle time in minutes. Metal Removal rate in MM3/Minute = Metal Removal rate in Grams/Minute Divided by Density of Work Piece in MM3/ Grams. All the experiments have conducted and the results and calculations of MRR have been tabulated in the Table III. Fig. 3. Profile Turning

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Full Paper Proc. of Int. Conf. on Advances in Mechanical Engineering 2012 TABLE III. EXPERIMENTATION AND OBSERVATION TABLE

Graph. 1. Graph of Response Diagram for MRR

In practice MRR should be high, thus Taguchi method refers to select the process parameter having more S/N ratio. From Table IV and Graph 1, all level totals has been compared and combination yielding the highest combined S/N ratio has selected for maximum metal removal rate. In this experiment, S3-F3-D3 combination yields the maximum metal removal rate. This is the optimal levels combination of factors for turning operation in CNC for aluminium material.

The objective of using the S/N ratio as a performance measurement is to develop products and process insensitive to noise factor. The sample calculations for the first experiment have been shown above. The experimental values of MRR and S/N ratios have tabulated in the Table III.

A. Analysis of Taguchi Design for MRR To know the effects of the four process parameters the overall mean value of S/N ratio is calculated as following:

VI. RESULTS AND DISCUSSION The response table, which contains the sums of the S/N ratios for each level and for each factor has been shown in the Table IV. TABLE IV. LEVEL T OOLS O F S/N R ATIO FOR EACH FACTOR

Now all three levels of every factor are equally represented in the nine experiments. Thus ‘M’ is a balanced overall mean over the experimental region. The ‘effects of factor level’ has defined as the deviation it causes from the overall mean. The average S/N ratio for the experiment has been denoted by MS1, MS2 and MS3 for the speed factor, MS1 can be given as:

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Full Paper Proc. of Int. Conf. on Advances in Mechanical Engineering 2012 [3]

K. Kadirgama, M.M. Noor. et. Al., Optimization of Surface Roughness in End Milling on Mould Aluminium Alloys (AA6061-T6) Using Response Surface Method and Radian Basis Function Network. Jordan Journal of Mechanical and Industrial Engineering, Volume 2, Number 4, December 2008, pp. 209 – 214. [4] Basim A. Khidhir and Bashir Mohamed (2010) Study of cutting speed on surface roughness and chip formation when machining nickel-bsed alloy, Journal of Mechanical Science and Technology, Vol. 24 (5), pp. 1053 – 1059. [5] Park, S.H. Robust Design an d An alysis for Qu alit y Engineering; Chapman & Hall, London, 1996. [6] Phadke, M.S., Quality Engineering Using Design of Experiment, Quality Control, Robust Design and T h e Taguchi Method; Wadsworth & Books, California, 1988. [7] Ghani, J. A., Choudhury, I.A., Hasan, H.H. Application of Taguchi Method in Optimization of End Milling Parameters, Journal of Material Processing Technology (2004) 145: 84–92. [8] Liu, C. H., Andrian, C., Chen, C.A., Wang, Y.T. Grinding Force Control in Automatic Surface Finish System, Journal of Materials Processing Technology (2005) 170: 367–373. [9] Kaladhari M. and Subbaiah K.V, “Application of Taguchi approach and Utility Concept in solving the Multi-objective Problem when turning AISI 202 Austenitic Stainless Steel”, Journal of Engineering Science and Technology Review 4 (1) (2011) 55-61. [10] Jafar Zare and Afsari Ahmad, “application of Taguchi method in the optimization of cutting parameters for surface roughness in turning”, Proceedings of academic journal of manufacturing engineering, vol. 8, (2010), issue 3. [11]Pramod K. Shahabadkar, N.B.Gurule, Kunal patil and Dr.K.N.Nandurkar “Experimental Investigations on EDM Using Taguchi Technique for Optimization of Process Parameters” Proceedings of International Conference and 22nd AIMTDR “ Manufacturing Technologies and systems for competitive Manufacturing” December-2006 organized by IIT Rorkee-India, pp. 941-946. [12] S. Thamizhmanii,Journal,Analyses, S. Saparudin, S. Hasan,” Analysis of Surface Roughness by Turning Process using Taguchi”, Journal of Achievements in Materials and Manufacturing Engineering, volume 20, ISSUES 1-2, JanuaryFebruary, 2007 pp. 503-506. [13] Lan T. S., Lo C. Y., Wang M. Y. and Yen A. Y., Multi Quality Prediction Model of CNC Turning Using Back Propagation Network, Information Proceeding of American Society of Mechanical Engineers, 2008. [14] Tash M., Samuel F. H., Mucciardi F., Doty H. W,, Valtierra S., (2006), Effect of metallurgical parameters on the machinability of heat-treated 356 and 319 aluminium alloys, J. Materials Science and Engineering, Vol. 434, pp. 207 – 217. [15] Sanyilmaz M., (2006), A application Taguchi method on experimental design and quality dvelopment, Master Thesis, Applied Natural Science Institute, Dumlupinar University, Kutahya.

Similarly the MS2 and MS3 can be calculated and represented in Table 5. MF1, MF2, MF3 and MD1, MD2, MD3 represents the average S/N ratio for feed and depth of cut factor. All these values have calculated and shown in the Table V. TABLE V. AVERAGE S/N RATIO FOR EACH FACTOR AT EACH LEVEL

The basic criterion for experimental data is S/N ratio to obtain biggest number of cycle values according to Taguchi method. The S/N ratio should have maximum values that were obtained from the calculations. From now, on level of valued speed, feed and depth of cut factors are selected from Table 3 to determine optimum cutting parameters of experiments at the same circumstances presented by Sanyilmaz [15]. From the results mentioned in Table V, it has been observed that 3 levels of all factors including speed, feed and depth of cut have higher value for material removal rate. Values for every given factor in Table 4 are defining the differences between highest S/N ratio and lower S/N ratio of factor from the highest value in Table 5 it is considered that depth of cut factor has the highest effect, next significant factor is speed followed by feed. CONCLUSIONS The paper has demonstrated an application of the Taguchi method for investing the effects of cutting parameters on material removal rate in turning aluminium metal. L9 orthogonal array has been constructed for three different levels of cutting parameters, which are speed, feed and depth of cut. The results of the experiments have performed according to L9 orthogonal array, S/N ratio by using bigger is better S/N ratio equation. According to maximum S/N ratio, the optimum cutting parameter for material removal rate having highest significant factor is depth of cut and the next significant factor is speed and least is feed. REFERENCES [1]

P .N. Rao, “Manufacturing Technology, Tata McGraw Hill publication, 2008, India. [2] Sanjit moshat, Saurav data, Ashish bandopaddhayay, Pradip kumar pal, (2010), Optimization of CNC milling process parameters using PCA based Taguchi Method, International Journal of Engineering Science and Technology, Vol. 2, No. 1, pp. 92 – 102.

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