Application of Taguchi method for predicting Springback in V-bending of aluminum alloy AA5052 strip

Transcription

1 Journal of Scientific Research and Development 3 (7): 91-97, 216 Available online at ISSN JSRAD Application of Taguchi method for predicting Springback in V-bending of aluminum alloy AA552 strip J. S. Ling, A. B. Abdullah* and Z. Samad School of Mechanical Engineering, Universiti Sains Malaysia, 143 Nibong Tebal, Pulau Pinang, Malaysia Abstract: In manufacturing industries, sheet metal is widely used to bend into complex shapes with high precision. The one of the common problem is springback. This paper studies the effect of cutting type, rolling direction and thickness of aluminium strip to springback using V bending method. The significance of the parameters to the springback pattern will be based on Taguchi Method. From this study, it is found that thickness of the specimen is the parameters that affect the springback the most, followed by the rolling direction. The cutting types give the least effect to the springback. Based on the measurement, cutting types will not significantly affect the hardness of the cutting edge. Nevertheless, it significantly affects the surface roughness but still no effect to the springback. Key words: Springback, Cutting types, Rolling direction, Thickness, Taguchi method 1. Introduction *In modern manufacturing industries, sheet metal had been popular and widely used as a material in aerospace, defence and automotive industries. One of the problems with automotive body part stamped from sheet metal is the tendency to have uncontrollable springback. The amount of springback is mainly influenced by various process parameters such as material properties including elastic modulus, yield strength, tensile strength and anisotropy induced from rolling, thickness of the sheet metal, friction, blank holder force and tooling geometry (Hamuoda et al., 23; Kalpakjian and Schmid, 21; Kumar et al., 21; Fu et al., 211; Chongthairungruang et al., 212; Barros et al., 213; Srinivasan et al., 213; Tang et al., 213; Abdullah et al., 214; Lee et al., 215; Jiang and Dai, 215; Zhang et al., 215). The discrepancy of the shapes between the deep drawn product and the designated one due to springback must be compensated at the design stage in order to guarantee its functionality and assemblability. However, it is difficult to predict and estimate the amount of compensation (Tekaslan, 28). Many researchers have studied the influence of process parameters on springback to improve accuracy of the stamped parts. Bakhshi-Jooybari (28) used different orientation angle of specimen sheet metal in order to determine the effect of anisotropy to springback. Thipprakmas (211) identified process parameter design of springback and spring-go in v-bending process using Taguchi technique. In his study, process parameters of bending angle, material thickness and punch radius were investigated. In others works, Tekiner (23) studied influence of various thickness and materials * Corresponding Author. by using V-bending method. Thipprakmas (211) concluded that the increase of hardness of sheet will increase the springback and found that an aluminium sheets (AA72-T6) has the biggest springback factor. Sharad (214), found that his investigation is in agreement with the others, where the springback is affected by sheet thickness, material properties and tooling geometry. In preparation of the specimen, metal strip can be either cut by shear cutting, EDM wire cutting or milling method. In general, these methods have different method of cutting, surface roughness, temperature gradient and strain hardening (Hargrove and Ding, 27; Kalpakjian and Schmid, 21; Davoodi et al., 214; Azhiri et al., 214) at the cutting edge. The main objective of this paper is to study the effect of cutting types to the springback. While the other two parameters i.e. rolling direction and thickness of the specimen were carried out as a benchmark in order to ensure reliability of the method used in the study. Methodology The study will be divided into three stages. Firstly preparation of the specimen, experimental setup and ends experimental procedure. Specimen preparation The Aluminium AA552 strips with dimensions of 3 6 mm were used in this experiment. Three levels of parameters, i.e. cutting types, rolling direction and material thickness are the main concern in this investigation. The parameters and variables are shown in Table 1. 91

2 To avoid large number of the experiment, the Taguchi method has been used to design the experiment. Based on Table 2, the experiment consists of three parameters, and each parameter has three different levels. According to Taguchi method, a form of orthogonal array L 9 will be used for data collection as shown in table 3. Each of the experiment will repeated 1 times for accuracy of the data. Symbol Table 1: Experiment parameter and their levels Levels Parameter A Cutting types EDM Wire cutting Machining Shearing B Rolling direction 45 9 C Material thickness.5mm 1.5mm 3.mm Experiment Number Table 2: Experimental layout based on an L 9 orthogonal array Cutting Process of specimen (A) Wire Cutting Wire Cutting Wire Cutting 4 Milling Milling (Level 2) Milling (Level 2) Parameter Angle to rolling direction (B) Material thickness (C).5mm 1.mm 3.mm 1.mm 3.mm.5mm 3.mm.5mm 1.mm Parameter setting A1B1C1 A1B2C2 A1B3C3 A2B1C2 A2B2C3 A2B3C1 A3B1C3 A3B2C1 A3B3C2 As mentioned earlier, is a well-known technique that provides a systematic and efficient methodology for process optimization. Generally, the parameter optimization process of the Taguchi method is based on 7 steps of planning, conducting and evaluating result of the experiments to determine the most levels of parameters affect the springback. Those seven steps are given as follow: i. Identify the responses (springback) to optimize and parameter of the specimens. ii. Determine the number of levels for each parameter of the specimens. iii. Select an appropriate orthogonal array based on the number of parameter and level. iv. Conduct an experiment randomly based on the arrangement of the orthogonal array. v. Analysis the experimental result using the ANOVA test. vi. Find the optimal level for each of the parameter of the specimens. vii. Make a conclusion of the experiment. Experiment Apparatus For V-bending experiment, the apparatus used is universal testing machine (UTM), hydraulic machine, and die set. Experiment Procedure The procedures of V-bending experiment to determine the springback of the specimen are shown below: 1. The die and the puncher set are placed on the universal testing machine (UTM). 2. The one of the specimen with 3 6mm dimensions are putted on the bottom die set. 3. The die and puncher set are compressed by UTM machine until the puncher touched top surface of the specimen. 4. Then, the specimen was bent with stroke distance 7mm and speed 5mm/min. 5. The step 1 to step 4 is repeated by changing the specimen with another 8 specimens. 6. After that, remaining specimens was bent by using hydraulic machine and set up as shown in the Figure The specimen was bent with stroke distance 7mm with the aid of dial indicator. 8. The angle of the springback can be obtained by subtract final angle with initial angle of the specimens using profile projector. Three sample of specimen for different cutting process will be selected to get the average surface roughness. 92

3 Hardness (HV) Ling et al/ Journal of Scientific Research and Development, 3 (7) 216, Pages: V-bending Punch 4 2 Wire cutting Shearing Machining Fig. 1: V-bending set up on hydraulic press machine Results and discussion Dial indicator The results in term of average of springback were obtained after conducting the V-bending test for all specimens. In the experiment, three types of cutting process i.e. wire cutting, shear cutting and milling. Heat input during cutting process will causes hardness distribution on the specimen due to changes of the grain size of the material. The different hardness of specimen will affect modulus of elasticity of specimens. So, hardness of material will directly affect the accuracy of the springback in this experiment. The purpose of hardness test is to compare the hardness between the specimens. The hardness value for EDM wire cutting is 67.68, shear cutting is and milling is Based on the Figure 2, the hardness value between different types of cutting processes is approximately the same. Theoretically, the hardness value (Vickers) for Aluminium alloy AA552 is 68HV. Therefore, hardness of the specimens is considering not significant. Cutting Type Fig. 2: Hardness versus cutting types of aluminium AA552 specimen In this experiment, three types of cutting which are EDM wire cutting, shear cutting and milling were studied. The difference in terms of surface roughness of the cut section will be further observed. Table 3 show the result of average surface roughness, R a of the three different cutting process of the material. Theoretically, surface roughness will affect the internal stress of the specimen. But based on the result, the cutting process that produces highest surface roughness is shear cutting with R a equal to 6.377μm followed by EDM wire cutting with surface roughness μm. Then, the surface roughness for machining is μm. Therefore, the cutting surface roughness is also not significant. Figure 3 (a), (b) and (c) show the image of the surface condition from each EDM wire cutting, milling and shear cutting respectively. No Table 3: Result of surface roughness Cutting process of specimen Average surface roughness, R a (μm) 1 EDM Wire Cutting Milling (a) (b) (c) Fig. 3: (a) Surface of specimen using EDM wire cutting, (b) milling and (c) shear cutting Each of the specimens represented one experiment in the orthogonal array in Table 2. The results of this experiment are summarized in Table 4. 93

4 Experiment number Table 4: Experimental result for V-bending Rolling Cutting types direction Thickness (mm) ( ) Wire cutting Wire cutting Wire cutting Milling Milling Milling Average springback ( ) Regression analysis The regression analysis is a statistical process to determine the relation between parameters (cutting types, rolling direction and thickness) and springback. The statistical software Minitab was used to derive the regression equation as below: Angle of Springback = *A1.141*A *A3.26*B1.276*B *B *C *C2 2.55*C3 R 2 =.9558 Where R 2 : Regression coefficient Based on the regression coefficient, R 2 obtained, R 2 is larger than.9 which means that the experimental data is satisfactory. Analysis of the S/N ratio Signal to noise ratio is measurement that used to identify controlled factor to reduce the level of uncontrollable factors (noise/error) in the experiment. The controlled factor is the experiment parameter that can be controlled. The uncontrolled factor is the unknown factor or background noise that will affect the result of the experiment. Theoretically, S/N ratio consist three categories of performance characteristic which are the larger the better, the nominal is better and the smaller the better. The performance characteristic used in this experiment was the smaller the better. The S/N ratio formula for the smaller the better is: S/N = 1 log 1 ( Y2 n ) = 1 log 1 (MSD) Where n: number of measurements in a trial = 1 Y: Amount of springback (angle) MSD: Mean square deviation The S/N ratio for each of the experiment number are calculated and tabulated in Table 5. Regardless of the optimal performance, a greater S/N value the better the performance. Based on the Figure 4, the optimal springback can be obtained when the parameter of specimen is set at A1, B2 and C2. Table 6 show the S/N ratio of springback by factor level. Based on the Table 7, the delta for thickness of the specimen is the highest which is The thickness of the specimen is the main parameters that affect the springback in this experiment. Table 5: S/N ratio for each of the experiment Experiment number Average amount of Mean square springback deviation (MSD) S/N ratio Table 6: S/N ratio of springback by factor level Level Cutting Types Rolling direction Thickness Delta Rank

5 Fig. 4: Effect of parameters on springback Analysis of variance (ANOVA) ANOVA is a statistical analysis used to determine which parameter significantly affected the quality characteristic. Table 6 show the ANOVA result for this experiment. The sum of square, SS was calculated from the following formula: n SS = (x ij x i ) 2 i=1 Where n: number of experiments in the orthogonal array x ij : Angle of springback for the i th experiment x i : Mean of angle of springback the i th experiment The total sum of the square, SS T is the summation of sum of the square, SS d and sum of squared error SS e. Mean square, MS is equal to SS T divide by degree of freedom, DF of process parameter. Statistically, there is a tool called an F Test which used to determine which process parameters have the most significant effect to the quality. The F-value need calculate to performing the F Test. The F Value is the ratio of mean square of the process parameter, MS p to the mean square error, MS e.theoretically, the F value more than 4 means that the change of the process parameter has significant effect to the quality characteristic. In this experiment, the F value for parameter (thickness of the specimen) is which is greater than 4. In F test, the F value for the parameter greater than 4 means that the change of the process parameter has significant effect on the quality characteristic according to Yang and Tarng (1998). Therefore, thickness of the specimen is main significant effect on the quality characteristic. Table 7 show the result of ANOVA for angle of springback with 95% of confidence level. The P- Value is used to determine the significant level. Usually, P value is less than significant level of.5 means null hypothesis is rejected and the effect of parameter is significant. The P-value for parameter C is.45 which is less than.5. So, effect parameter C is significant to the result of the experiment. The percentage of contribution, P was calculated as the following formula: P = ( SS d SS T ) 1% The percentage of contribution of parameter C is the highest (94.45%) followed by parameter A 1.95% and parameter B 1.4%. Conclusions In conclusion, this study has presented the application of Taguchi method to analyse the effect of several parameters on the springback. The main contribution of the study is to observe the effect of cutting types in the preparation of the specimen to springback pattern. Based on the result obtained, the thickness of specimen is the main parameters that affect the springback significantly. The contribution of the cutting type, rolling direction and thickness of the specimen to springback is 1.95%, 1.4% and 94.45% respectively. The cutting types did influence the hardness and surface roughness of the cutting edge of the specimens but not yet affect the springback. 95

6 Symbol Parameter Degree of Freedom, DF Table 7: ANOVA result Sum of square, SS Mean Square, MS F Value P- value Contribution (%) A Cutting Types B Rolling direction C Thickness of specimen Error Total Acknowledgement The authors want to acknowledge Ministry of Higher Education and Universiti Sains Malaysia for their sponsorship through Fundamental Research Grant Scheme. References Abdullah, A. B. and Samad, Z. (213) An experimental investigation of springback of AA661 aluminum alloy strip via V-bending process, IOP Conference Series: Materials Science and Engineering Vol. 5 No. 1, Azhiri, R. B., Teimouri, R., Baboly, M. G. and Leseman, Z. (214) Application of Taguchi, ANFIS and grey relational analysis for studying, modeling and optimization of wire EDM process while using gaseous media The International Journal of Advanced Manufacturing Technology Vol. 71 No. 1-4, pp Bakhshi-Jooybari, M., Rahmani, B. Daeezadeh, V. and Gorji, A. (28) 'The Study of Spring-Back of Ck67 Steel Sheet in V-Die and U-Die Bending Processes', Materials and Design Vol. 9, pp Barros, P. D., Oliveira, M. C., Alves, J. L. and Menezes, L. F. (213) Pre-strain effect on springback of 2D draw bending Int. J. of Materials Engineering Innovation Vol. 4 No. 2, pp Davoodi, B. and Desari, B. Z. (214) Assessment of forming parameters influencing spring-back in multi-point forming process: A comprehensive experimental and numerical study, Materials & Design Vol. 59, pp Fu, X. J., Li, J. J. and Xu, G. (211) Experimental study on springback of high strength sheet metals, Materials Research Innovations Vol. 15 No. s1, pp. s475 - s477 Hamouda, A.M.S., Abu Khadra, F., Hamdan, M.M., Imhemed, R.M. and Mahdi, E. (24) Springback in v-bending: a finite element approach Int. J. of Materials and Product Technology Vol. 21 No. 1/2/3, pp Hargrove, S. K. and Ding, D. (27) Determining cutting parameters in wire EDM based on workpiece surface temperature distribution, The 96 International Journal of Advanced Manufacturing Technology September 27, Vol. 34 No. 3-4, pp Jiang, H. J. and Dai, H. L. (215) A novel model to predict U-bending springback and timedependent springback for a HSLA steel plate, The International Journal of Advanced Manufacturing Technology, DOI 1.17/s Kalpakjian, S. and Schmid, S. R. Manufacturing Engineering and Technology, Pearson, 4th Edition, New York, USA. 21. Kumar, A., Viswanath, P., Mahesh, K., Swati, M., Kumar, P. M. V., Abhijit, A. and Singh, K. S. (21) Prediction of springback in V-bending and design of dies using finite element simulation, Int. J. of Materials and Product Technology Vol. 39 No. 3/4, pp Lee, J. Y., Barlat, F. and Lee, M. G. (215) Constitutive and friction modeling for accurate springback analysis of advanced high strength steel sheets, International Journal of Plasticity Vol. 71, pp Rama, R. S. and Padmanabhan, G. (212) '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 No. 3, pp Sharad, G. and Nandedkar, V. M. (214) 'Springback in Sheet Metal Bending', Journal of Mechanical and Civil Engineering, pp Srinivasan, R., Vasudevan, D. and Padmanabhan, P. (213) Application of response surface methodology for predicting springback in air bending of electro galvanised steel sheets Int. J. of Materials Engineering Innovation Vol. 4 No.1, pp Tang, L., Wang, H. and Li, G. (213) Advanced high strength steel springback optimization by projection-based heuristic global search algorithm Materials & Design Vol. 43, pp Tekaslan, O., Gerger, N. and Seker, U. (28) 'Determination of Spring-Back of Stainless Steel

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