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International Journal of Science Engineering and Technology Vol. 1, No. 3, 2008ISSN: 1985-3785Available online at: www.ijs...
G. Akhyar et al. / Int. J. Sci. Eng. Tech. Vol. 1, No. 3, 2008, 60-66     Taguchi defines as the quality of a product, in ...
G. Akhyar et al. / Int. J. Sci. Eng. Tech. Vol. 1, No. 3, 2008, 60-66           Table 2. Experimental results for surface ...
G. Akhyar et al. / Int. J. Sci. Eng. Tech. Vol. 1, No. 3, 2008, 60-66                   Table 3. Average for S/N ratio and...
G. Akhyar et al. / Int. J. Sci. Eng. Tech. Vol. 1, No. 3, 2008, 60-66    The optimum condition in turning of              ...
G. Akhyar et al. / Int. J. Sci. Eng. Tech. Vol. 1, No. 3, 2008, 60-66observed on the graph of tool grade factor for       ...
G. Akhyar et al. / Int. J. Sci. Eng. Tech. Vol. 1, No. 3, 2008, 60-66    Processing Technology (2004) 145: 84–            ...
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  1. 1. International Journal of Science Engineering and Technology Vol. 1, No. 3, 2008ISSN: 1985-3785Available online at: www.ijset.org© 2008 ILRAM PublisherApplication of Taguchi Method in the Optimization of TurningParameters for Surface Roughness1 G. Akhyar, 1C.H. Che Haron, 1J.A. Ghani1 Department of Mechanics and Materials, Universiti Kebangsaan MalaysiaAbstractThe quality of design can be improved by improving quality and productivity in company-wide activities.Taguchi’s parameter design is an important tool for robust design, which offers a simple and systematicapproach to optimize a design for performance, quality and cost. Taguchi optimization methodology is applied tooptimize cutting parameters in turning Ti-6%Al-4%V extra low interstitial with coated and uncoated cementedcarbide tools under dry cutting condition and high cutting speed. The turning parameters evaluated are cuttingspeed of 55, 75, and 95 m/min, feed rate of 0.15, 0.25 and 0.35 mm/rev, depth of cut of 0.10, 0.15 and 0.20 mmand tool grades of K313, KC9225 and KC5010, each at three levels. The analysis of results show that theoptimal combination of parameters are at cutting speed of 75 m/min, feed rate of 0.15 mm/min, depth of cut of0.10 mm and tool grade of KC9225. The cutting speed and tool grade have a significant effect on surfaceroughness are 0.000 and have a contribution are 47.146% and 38.881%, respectively. At optimal condition,contribution of each cutting parameter on surface roughness is reached at 20.47 from tool grade, 21.01 from feedrate, 11.54 from depth of cut and 11.17 from cutting speed.KEYWORDS: Taguchi Method, Turning, Ti-6Al-4V ELI and Coated Carbide Tool.1. Introduction cutting speed of 355 m/min, low feed rate of 0.1mm per tooth and low depth of cut of 0.5 mm. The quality of design can be improved by Application of Taguchi’s method for parametricimproving the quality and productivity in company- design was carried out to determine an ideal feedwide activities. Those activities concerned with rate and desired force combination Although smallquality, include in quality of product planning, interactions exist between a horizontal feed rate andproduct design and process design [1, 3]. Robust desired force, the experimental results showed thatdesign is an engineering methodology for obtaining surface roughness decreases with a slower feed rateproduct and process condition, which are minimally and larger grinding force, respectively [7].sensitive to the various causes of variation, and Conceptual S/N ratio approach of Taguchi methodwhich produce high-quality products with low provides a simple, systematic and efficientdevelopment and manufacturing costs [1]. methodology for optimizing of process parametersTaguchi’s parameter design is an important tool for and this approach can be adopted rather than usingrobust design. It offers a simple and systematic engineering judgment. Furthermore, the multipleapproach to optimize design for performance, performance characteristics such as tool life, cuttingquality and cost. Signal to noise ratio and force, surface roughness and the over allorthogonal array are two major tools used in robust productivity can be improved by useful tool ofdesign. Signal to noise ratio, which measures Taguchi method [8].quality with emphasis on variation, and orthogonal This paper describes the turning of Ti-6Al-4Varrays, which accommodates many design factors ELI with parameters of turning at three levels andsimultaneously [1, 2]. four factors each. The main objective is to find a Taguchi method offers the quality of product is specific range and combination of turningmeasured by quality characteristics such as: parameters and interaction to achieve the lowestnominal is the best, smaller is better and larger is surface roughness value.better [1, 3]. Optimization using Taguchi method inend milling using conceptual S/N ratio approachand Pareto ANOVA proceed, the Taguchi’s robust 2. Taguchi method, design of experiment, anddesign method is suitable to analyze the metal experimental detailscutting problem. Ghani et.al [6] found that the 2.1. Taguchi methodconceptual S/N ratio and Pareto ANOVAapproaches for data analysis draw similarconclusion in process end milling use at highCorresponding Author: G. Akhyar, Department of Mechanics and Material Engineering, University Kebangsaan Malaysia, Kuala Lumpur, Malaysia, E-mail: sati1771@gmail.com
  2. 2. G. Akhyar et al. / Int. J. Sci. Eng. Tech. Vol. 1, No. 3, 2008, 60-66 Taguchi defines as the quality of a product, in 3. Factors, which do not affect the S/N ratio orterms of the loss imparted by the product to the process mean.society from the time the product is shipped to the In practice, the target mean value may changecustomer [2]. Some of these losses are due to during the process development applications indeviation of the product’s functional characteristic which the concept of S/N ratio is useful are thefrom its desired target value, and these are called improvement of quality through variabilitylosses due to functional variation. The uncontrollable reduction and the improvement of measurement.factors, which cause the functional characteristics of The S/N ratio characteristics can be divided intoa product to deviate from their target values, are three categories when the characteristic iscalled noise factors, which can be classified as continuous: nominal is the best, smaller the betterexternal factors (e.g. temperatures and human errors), and larger is better characteristics.manufacturing imperfections (e.g. unit to unitvariation in product parameters) and product 2.2. Experimental detailsdeterioration. The overall aim of quality engineeringis to make products that are robust with respect to all The experiments were carried out with fournoise factors. factors at three levels each, as shown in Table 1. Taguchi has empirically found that the two stage The fractional factorial design used is a standardoptimization procedure involving S/N ratios, indeed L27 (313) orthogonal array with 20 degree ofgives the parameter level combination, where the freedom [1]. This orthogonal array is chosen duestandard deviation is minimum while keeping the to its capability to check the interactions amongmean on target [2, 3, 4]. This implies that factors.engineering systems behave in such a way that the The machining trials were carried out on themanipulated production factors that can be divided lathe machine (Colchester T4 with maximum 6000into three categories: rpm) in dry condition, as recommended by the tool1.Control factors, which affect process variability as supplier for the specific work material. The threemeasured by the S/N ratio. inserts used were uncoated carbide tool K3132. Signal factors, which do not influence the S/N (WC-Co), coated carbide tool KC9225 (TiN-ratio or process mean. Al2O3-TiCN-TiN) CVD and KC5010 (TiAlN) PVD, respectively. Table 1. Factors and levels used in the experiment Levels Factors 0 1 2 A- Cutting speed (m/min) 55 75 95 B- Feed rate (mm/rev) 0.15 0.25 0.35 C- Depth of cut (mm) 0.10 0.15 0.20 D- Tool type K313 KC9225 KC5010 The maximum flank wear land (VB) was depth of cut and tool grade) to achieve low value of measured at regular interval of one pass machining the surface roughness. The experimental data for using Mitutoyo Tool Maker Microscope with 20x the surface roughness values and the calculated magnification. The surface roughness of machined signal-to-noise ratio are shown in Table 2. The S/N surface was then measured accordingly surface ratio values of the surface roughness are calculated, roughness tester model Mpi Mahr Perthometer. The using the smaller the better characteristics [3, 4]. turning process was stop when VB reached 0.2 mm. 3. Results and Discussions 1 S N  10 log n  y  2 (1) 3.1 Signal to Noise Ratio (S/N) The main objective of the experiment is to optimize the turning parameters (cutting speed, feed rate, 61
  3. 3. G. Akhyar et al. / Int. J. Sci. Eng. Tech. Vol. 1, No. 3, 2008, 60-66 Table 2. Experimental results for surface roughness and its calculated S/N ratios. Factor Surface S/N ratio for Exp. Designation roughness, surface run. A B C D Ra (m) roughness 1 0 0 0 0 A0B0C0D0 1.71 -4.66 2 0 0 1 1 A0B0C1D1 1.17 -1.364 3 0 0 2 2 A0B0C2D2 2.50 -7.959 4 0 1 0 1 A0B1C0D1 1.09 -0.749 5 0 1 1 2 A0B1C1D2 4.94 -13.875 6 0 1 2 0 A0B1C2D0 3.48 -10.832 7 0 2 0 2 A0B2C0D2 6.01 -15.578 8 0 2 1 0 A0B2C1D0 6.49 -16.245 9 0 2 2 1 A0B2C2D1 2.82 -9.067 10 1 0 0 0 A1B0C0D0 0.53 5.514 11 1 0 1 1 A1B0C1D1 1.56 -3.863 12 1 0 2 2 A1B0C2D2 1.44 -3.168 13 1 1 0 1 A1B1C0D1 4.67 -13.387 14 1 1 1 2 A1B1C1D2 3.02 -9.601 15 1 1 2 0 A1B1C2D0 0.97 0.264 16 1 2 0 2 A1B2C0D2 3.94 -11.91 17 1 2 1 0 A1B2C1D0 2.56 -8.165 18 1 2 2 1 A1B2C2D1 6.19 -15.834 19 2 0 0 0 A2B0C0D0 2.02 -6.108 20 2 0 1 1 A2B0C1D1 1.73 -4.761 21 2 0 2 2 A2B0C2D2 1.10 -0.828 22 2 1 0 1 A2B1C0D1 3.56 -11.029 23 2 1 1 2 A2B1C1D2 2.37 -7.495 24 2 1 2 0 A2B1C2D0 4.31 -12.69 25 2 2 0 2 A2B2C0D2 1.29 -2.212 26 2 2 1 0 A2B2C1D0 4.26 -12.589 27 2 2 2 1 A2B2C2D1 5.20 -14.321 Table 2 shows the actual data of surface Average S/N ratio for each level of experimentroughness along with its computed S/N ratio is calculated based on the value of Table 1, andvalue. Whereas the S/N ratio for each levels of is shown in Table 2. The different values of thethe surface roughness as shown in Table 3. In S/N ratio between maximum and minimumthe standard L27 (313) orthogonal array, factor (main effect) are also shown in Table 2. TheA, B, C and D are arranged in columns 1,2, 5 feed rate and the tool grade are two factors withand 9, respectively. Whereas interaction factors the highest different in values of 9.553 andbetween the cutting speed and feed rate (AxB), 8.445, respectively. Based on Taguchithe cutting speed and depth of cut (AxC) and prediction that the bigger different in value ofthe feed rate and depth of cut (BxC) are S/N ratio shows a more effect on surfacearranged in columns 3, 6 and 8, respectively. roughness or more significant. Therefore, it can be concluded that, increase changes the feed3.2 Analysis of variance for S/N ratio rate reduces the surface roughness significantly. Furthermore, the tool geometry changes, mainly Taguchi recommends to analyze data tool nose radius, increase or decrease theusing the S/N ratio that will offer two surface roughness significantly.advantages; it provides a guidance for selection The result of S/N ratio analysis for thethe optimum level based on least variation surface roughness values, which was calculatedaround on the average value, which closest to using Taguchi Method, is shown in Table 3.target, and also it offers objective comparison Then, analysis of variance is shown in Table 4,of two sets of experimental data with respect to which consists of DF (degree of freedom), Sdeviation of the average from the target [3]. The (sum of square), V (variance), F (variance ratio)experimental results are analyzed to investigate and P (significant factor) [3, 4]. In mostthe main effects and differences between the engineering cases, the significant value selectedmain effects of level 0, 1 and 2 on the variables. was 5% ( = 0.05). 62
  4. 4. G. Akhyar et al. / Int. J. Sci. Eng. Tech. Vol. 1, No. 3, 2008, 60-66 Table 3. Average for S/N ratio and main effect of surface roughness Level average No. columns Variable Factors Designation Max. - Min. Rank 0 1 2 1 Cutting speed A -8.918 -7.221 -8.213 1.697 4 2 Feed rate B -3.022 -8.757 -12.574 9.553 1 5 Depth of cut C -7.063 -8.961 -8.329 1.897 3 9 Tool grade D -9.403 -3.252 -11.697 8.445 2 Table 4. ANOVA analysis of S/N ratio for surface roughness Sum of Variance Percent Contribution No Varible Factors Designation DF F Square (S) (V) (P) (%) 1 Cutting speed A 2 13.083 6.542 1.36 0.327 1.482 2 Feed rate B 2 416.179 208.089 43.13 0.000 47.146 3 Cutting speed x feed rate AxB 4 44.858 11.214 2.32 0.170 5.082 5 Depth of cut C 2 16.800 8.400 1.74 0.253 1.903 6 Cutting speed x depth of cut AxC 4 5.958 1.490 0.31 0.862 0.675 8 Feed rate x depth of cut BxC 4 13.697 3.424 0.71 0.614 1.552 10 Tool grade D 2 243.223 171.611 35.57 0.000 38.881 Error 6 28.949 4.825 3.279 Total 26 100 Table 4 shows that the significant value 0.170 from AxB, 0.862 from AxC and 0.614%of the feed rate and tool grade (P) is 0.000. It from BxC. While, a contribution for eachmeans that the feed rate and tool grade interaction is small.influences significantly on the surface The most significant factor, whichroughness value at significant value of 0.05. affects the surface roughness measured inIn addition to P value for the cutting speed and turning Ti-6Al-4V, is the feed rate thereforedepth of cut are insignificant. The feed rate and the quality of surface roughness can bethe tool grade have a contribution for the controlled by a suitable feed rate value.surface roughnesses are 47.146 % and Previous researchers suggest similar results.38.881%, respectively. From this result, it can They claimed that the surface roughness wellbe concluded that the feed rate is more strongly depends on the feed rate followed bysignificant factor and give most contribution the cutting speed. Jaharah et al. [6]on the surface roughness. Bhattacharyya found recommended to obtain better surface finishthat the surface roughness was primarily for specific test range in end milling was usedependent on the feed rate and the nose radius of high cutting speed (355 m/min), low feedof tool [9]. The nose radius related to tool rate (0.1 mm/tooth) and low depth of cut (0.5grade and tool geometry. Since three types of mm).tool were applied in this experiment have Table 5 shows level of contributiondifferent tool nose radius, so effect of tool nose each parameter and interaction on surfacegeometry changes on surface roughness was roughness values for estimating optimumsignificant. condition. The biggest contribution is from The interaction between the cutting feed rate of 0.15 mm/rev (21.01%), and thenspeed and feed rate (AxB), the cutting speed followed by tool grade of KC9225 (20.47%).and depth of cut (AxC) and the feed rate and The contribution from depth of cut and cuttingdepth of cut (BxC) are also insignificant. speed were 11.54% and 11.17%, respectively.These significant values of interaction are 63
  5. 5. G. Akhyar et al. / Int. J. Sci. Eng. Tech. Vol. 1, No. 3, 2008, 60-66 The optimum condition in turning of factors is the cutting speed and feed rate ofaerospace material Ti-6Al-4V ELI which level 1, the cutting speed and depth of cut ofproduces a low surface roughness is at cutting level 0, and the feed rate and depth of cut ofspeed of level 1 (0.75m/min), feed rate of level level 0. The total contribution for optimum0 (0.15 mm/rev), depth of cut of level 0 (0.10 condition is 383,898 and its expected result ismm) and tool grade of level 1 (KC9225). 376.028.Meanwhile, optimum condition for interaction Table 5. Estimate of optimum condition for the smaller the better characteristics. Level of Factors description Levels Contribution Contribution (%) description Cutting speed (A) 0.75 1 42.881 11.17 Feed rate (B) 0.15 0 80.672 21.01 Cutting speed x federate (AxB) - 1 51.780 13.49 Depth of cut (C) 0.1 0 44.303 11.54 Cutting speed x depth of cut (AxC) - 0 43.300 11.28 Feed rate x depth of cut (BxC) - 0 42.360 11.03 Tool grade (D) KC9225 1 78.602 20.47 Contribution for all factors (total) 383.898 100.00 Current grand average of performance -7.87 Expected result at optimum condition 376.028 Main Effects Plot (data means) for SN ratios Cutting speed Feedrate -4 -6 -8 Mean of SN ratios -10 -12 0 1 2 0 1 2 Depth of cut T ype of tool -4 -6 -8 -10 -12 0 1 2 0 1 2 Signal-to-noise: Smaller is better Figure 1. Main effects for factors machining verse S/N ratio of surface roughness. The main effects for each level of parameter It can be seen from Figure 1 that B0 is theon surface roughness are shown in Figure 1. maximum value with –3.022 of S/N ratio, andThe best choice for machining titanium alloy is decrease dramatically to B1 (-8.757), and thenbased on S/N ratio as followed; at cutting speed follow to B2 (–12.574). For the graph of feedof -8.918 (A1), feed rate of -3.022 (B0), depth of rate, the slope between the horizontal and feedcut of -7.063 (C0) and tool grade of -3.252 (D1). rate line is bigger. It means that the feed rateThe best combination is A1B0C0D1 that means changes effected significantly on surfaceat low high cutting speed, low feed rate, low roughness, and the same trend can also bedepth of cut and CVD coated carbide tool. 64
  6. 6. G. Akhyar et al. / Int. J. Sci. Eng. Tech. Vol. 1, No. 3, 2008, 60-66observed on the graph of tool grade factor for (A1) and the feed rate at level 0 (B0) have aeach level. maximum value. Figure 2 shows the interaction between the It can be also seen from Table 5 that thecutting speed and feed rate (AxB), the cutting optimum predicted result for each interactionspeed and depth of cut (AxC) and the feed rate gives contribution is 13.49% from AxB, 11.28%and depth of cut (BxC). The S/N ratio value at from AxC and 11.03% from BxC. The(AxB)1 is a best interaction because of it gives contribution for all factors is 383.898, while thethe biggest delta value, and then followed by expected result at optimum condition is 376.028.interaction (AxC)0. The cutting speed at level 1 Interaction Plot (data means) for SN ratios 0 1 2 0 C utting speed -5 0 C utting spee d 1 -10 2 0 Feedrate 0 -5 1 F eedr ate 2 -10 0 Depth of cut -5 0 Depth of cut 1 -10 2 0 1 2 0 1 2 Signal-to-noise: Smaller is better Figure 2. Interaction S/N ratio for surface roughness with smaller is better4. Conclusions References From the findings of the following can be [1] Park, S.H. Robust Design and Analysisconcluded: for Quality Engineering; Chapman &1. Taguchi’s robust design method is suitable Hall, London, 1996. to optimize the surface roughness in turning [2] Phadke, M.S. Quality Engineering Ti-6Al-4V ELI. Using Design of Experiment, Quality2. The significant factors for the surface Control, Robust Design and The roughness in turning Ti-6Al-4V ELI were Taguchi Method; Wadsworth & Books, the feed rate and the tool grade, with California, 1988. contribution of 47.146% and 38.881%, [3] [Ranjit, R. Design of experiment Using respectively. The Taguchi Approach; John Wiley &3. The optimal condition for surface roughness Sons Inc., New York, 2001. in turning Ti-6Al-4V ELI was resulted at [4] Ranjit, R. A Primer on The Taguchi cutting speed of 75 m/min, feed rate of 0.15 Method, Society of Manufacturing mm/rev, depth of cut of 0.10 mm and CVD Engineers; Dearborn, Michigan, 1999. coated carbide with KC9225. [5] Bendell, T. Taguchi Method,4. The optimal interaction parameter was Proceedings of the European between the cutting speed and feed rate at Conference of Taguchi Method, level 1 (75 m/min). Elsevier, Amsterdam, July 13-24, 1988. [6] Ghani, J.A., Choudhury, I.A., Hasan, H.H. Application of Taguchi Method in Optimization of End Milling Parameters, Journal of Materials 65
  7. 7. G. Akhyar et al. / Int. J. Sci. Eng. Tech. Vol. 1, No. 3, 2008, 60-66 Processing Technology (2004) 145: 84– Parameters on Cutting Force and 92. Torque During Drilling of Glass-Fiber[7] Liu, C.H., Andrian, C., Chen, C.A., Polyester reinforce Composite, Journal Wang, Y.T. Grinding Force Control in of Composite Structures (2005) 71: Automatic Surface Finish System, 407–413. Journal of Materials Processing [9] Bhattacharyya. Metal cutting theory Technology (2005) 170: 367–373. and practice, New Central Book[8] Mohan, N.S., Ramachndra, A., Agency, Calcutta 1998. Kulkarni, S.M. Influence of Process 66

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