Journal of Scientific Research and Development 3 (7): 91-97, 2016 Available online at www.jsrad.org ISSN 1115-7569 © 2016 JSRAD

91

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

alloy AA5052 strip

J. S. Ling, A. B. Abdullah* and Z. Samad

School of Mechanical Engineering, Universiti Sains Malaysia, 14300 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., 2003; Kalpakjian and Schmid, 2010; Kumar et al., 2010; Fu et al., 2011; Chongthairungruang et al., 2012; Barros et al., 2013; Srinivasan et al., 2013; Tang et al., 2013; Abdullah et al., 2014; Lee et al., 2015; Jiang and Dai, 2015; Zhang et al., 2015). 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, 2008). Many researchers have studied the influence of process parameters on springback to improve accuracy of the stamped parts. Bakhshi-Jooybari (2008) used different orientation angle of specimen sheet metal in order to determine the effect of anisotropy to springback. Thipprakmas (2011) 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 (2003) studied influence of various thickness and materials

* Corresponding Author.

by using V-bending method. Thipprakmas (2011) concluded that the increase of hardness of sheet will increase the springback and found that an aluminium sheets (AA7020-T6) has the biggest springback factor. Sharad (2014), 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, 2007; Kalpakjian and Schmid, 2010; Davoodi et al., 2014; Azhiri et al., 2014) 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 AA5052 strips with dimensions

of 30×60 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.

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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 𝐿9 will be used for data collection as shown in table 3. Each of the experiment will repeated 10 times for accuracy of the data.

Table 1: Experiment parameter and their levels

Symbol Parameter Levels

1 2 3

A Cutting types EDM Wire cutting Machining Shearing

B Rolling direction 0° 45° 90°

C Material thickness 0.5mm 1.5mm 3.0mm

Table 2: Experimental layout based on an 𝐿9 orthogonal array

Experiment Number

Parameter Parameter setting Cutting Process

of specimen (A) Angle to rolling

direction (B) Material

thickness (C)

1 Wire Cutting

(Level 1) 0°

(Level 1) 0.5mm

(Level 1) A1B1C1

2 Wire Cutting

(Level 1) 45°

(Level 2) 1.0mm

(Level 2) A1B2C2

3 Wire Cutting

(Level 1) 90°

(Level 3) 3.0mm

(Level 3) A1B3C3

4 Milling (Level 2) 0°

(Level 1) 1.0mm

(Level 2) A2B1C2

5 Milling (Level

2) 45°

(Level 2) 3.0mm

(Level 3) A2B2C3

6 Milling (Level

2) 90°

(Level 3) 0.5mm

(Level 1) A2B3C1

7 Shear Cutting

(Level 3) 0°

(Level 1) 3.0mm

(Level 3) A3B1C3

8 Shear Cutting

(Level 3) 45°

(Level 2) 0.5mm

(Level 1) A3B2C1

9 Shear Cutting

(Level 3) 90°

(Level 3) 1.0mm

(Level 2) 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 30 × 60mm

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 1.

7. 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.

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Fig. 1: V-bending set up on hydraulic press machine

Results and discussion

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 69.54 and milling is 68.59. 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 AA5052 is 68HV. Therefore, hardness of the specimens is considering not significant.

Fig. 2: Hardness versus cutting types of aluminium

AA5052 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, 𝑅𝑎 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 𝑅𝑎 equal to 6.0377𝜇𝑚 followed by EDM wire cutting with surface roughness 3.2754𝜇𝑚. Then, the surface roughness for machining is 1.6145𝜇𝑚. 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.

Table 3: Result of surface roughness

No Cutting process of

specimen

Average surface roughness, 𝑹𝒂

(𝝁𝒎) 1 EDM Wire Cutting 3.2754 2 Milling 1.6145 3 Shear Cutting 6.0377

(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.

67.68 69.54 68.59

0

20

40

60

80

Wire cutting Shearing MachiningHar

dn

ess

(HV

)

Cutting Type

V-bending Punch

Dial indicator

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Table 4: Experimental result for V-bending

Experiment number

Cutting types Rolling

direction (°)

Thickness (mm) Average

springback (°)

1 2 3 4 5 6 7 8 9

Wire cutting Wire cutting Wire cutting

Milling Milling Milling

Shear Cutting Shear Cutting Shear Cutting

0 45 90 0

45 90 0

45 90

0.5 1.0 3.0 1.0 3.0 0.5 3.0 0.5 1.0

10.04 0.97 3.76 2.69 2.24 9.35 1.20

10.67 3.21

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 = 4.902 + 0.019*A1 – 0.141*A2 + 0.122*A3 – 0.260*B1 – 0.276*B2 + 0.537*B3 + 5.117*C1 – 2.612*C2 – 2.505*C3

𝑅2 = 0.9558 Where 𝑅2 : Regression coefficient Based on the regression coefficient, 𝑅2 obtained,

𝑅2is larger than 0.9 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 = − 10 ∗ log10(∑𝑌2

𝑛)

= −10 log10(𝑀𝑆𝐷)

Where n: number of measurements in a trial = 10 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 13.846. 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

springback Mean square

deviation (MSD) S/N ratio

1 10.04 100.8016 -20.0347

2 0.97 0.9409 0.2646 3 3.76 14.1376 -11.5038 4 2.69 7.2361 -8.5950 5 2.24 5.0176 -7.0050 6 9.35 87.4225 -19.4162 7 1.20 1.4400 -1.5836 8 10.67 113.8489 -20.5633 9 3.21 10.3041 -10.1301

Table 6: S/N ratio of springback by factor level

Level Cutting Types Rolling direction Thickness 1 -10.427 -10.067 -20.004 2 -11.672 -9.103 -6.158 3 -10.750 -13.679 -6.686

Delta 1.245 4.576 13.846 Rank 3 2 1

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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:

𝑆𝑆 = ∑(𝑥𝑖𝑗 − 𝑥�̅�)2

𝑛

𝑖=1

Where n: number of experiments in the orthogonal array 𝑥𝑖𝑗: Angle of springback for the 𝑖𝑡ℎ experiment

𝑥�̅�: Mean of angle of springback the 𝑖𝑡ℎ experiment The total sum of the square, 𝑆𝑆𝑇 is the summation

of sum of the square, 𝑆𝑆𝑑 and sum of squared error 𝑆𝑆𝑒 . Mean square, MS is equal to 𝑆𝑆𝑇 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, 𝑀𝑆𝑝 to the mean square

error, 𝑀𝑆𝑒 .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 21.38 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 0.05 means null hypothesis is rejected and the effect of parameter is significant. The P-value for parameter C is 0.045 which is less than 0.05. So, effect parameter C is significant to the result of the experiment. The percentage of contribution, P was calculated as the following formula:

𝑃 = (𝑆𝑆𝑑

𝑆𝑆𝑇) × 100%

The percentage of contribution of parameter C is the highest (94.45%) followed by parameter A 1.905% and parameter B 1.04%.

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 1.905%, 1.040% 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.

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Table 7: ANOVA result

Symbol Parameter Degree of Freedom,

DF

Sum of square,

SS

Mean Square,

MS F Value

P-value

Contribution (%)

A Cutting Types 2 0.105 0.0527 0.02 0.981 0.084

B Rolling

direction 2 1.298 0.6488 0.24 0.809 1.040

C Thickness of

specimen 2 117.839 58.9194 21.38 0.045 94.450

Error 2 5.511 2.7557 4.420

Total 8 124.753

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. (2013) ‘An experimental investigation of springback of AA6061 aluminum alloy strip via V-bending process’, IOP Conference Series: Materials Science and Engineering Vol. 50 No. 1, 012069.

Azhiri, R. B., Teimouri, R., Baboly, M. G. and Leseman, Z. (2014) ‘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 279-295

Bakhshi-Jooybari, M., Rahmani, B. Daeezadeh, V. and Gorji, A. (2008) 'The Study of Spring-Back of Ck67 Steel Sheet in V-Die and U-Die Bending Processes', Materials and Design Vol. 9, pp. 2410-2419.

Barros, P. D., Oliveira, M. C., Alves, J. L. and Menezes, L. F. (2013) ‘Pre-strain effect on springback of 2D draw bending’ Int. J. of Materials Engineering Innovation Vol. 4 No. 2, pp. 187 - 211.

Davoodi, B. and Desari, B. Z. (2014) ‘Assessment of forming parameters influencing spring-back in multi-point forming process: A comprehensive experimental and numerical study’, Materials & Design Vol. 59, pp. 103-114

Fu, X. J., Li, J. J. and Xu, G. (2011) ‘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. (2004) ‘Springback in v-bending: a finite element approach’ Int. J. of Materials and Product Technology Vol. 21 No. 1/2/3, pp. 124 - 136.

Hargrove, S. K. and Ding, D. (2007) ‘Determining cutting parameters in wire EDM based on workpiece surface temperature distribution’, The

International Journal of Advanced Manufacturing Technology September 2007, Vol. 34 No. 3-4, pp 295-299

Jiang, H. J. and Dai, H. L. (2015) ‘A novel model to predict U-bending springback and time-dependent springback for a HSLA steel plate’, The International Journal of Advanced Manufacturing Technology, DOI 10.1007/s00170-015-7274-0

Kalpakjian, S. and Schmid, S. R. Manufacturing Engineering and Technology, Pearson, 4th Edition, New York, USA. 2010.

Kumar, A., Viswanath, P., Mahesh, K., Swati, M., Kumar, P. M. V., Abhijit, A. and Singh, K. S. (2010) ‘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. 291- 301.

Lee, J. Y., Barlat, F. and Lee, M. G. (2015) ‘Constitutive and friction modeling for accurate springback analysis of advanced high strength steel sheets’, International Journal of Plasticity Vol. 71, pp. 113-135.

Rama, R. S. and 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 No. 3, pp.192-197.

Sharad, G. and Nandedkar, V. M. (2014) 'Springback in Sheet Metal Bending', Journal of Mechanical and Civil Engineering, pp. 53-56.

Srinivasan, R., Vasudevan, D. and Padmanabhan, P. (2013) ‘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. 35 - 56

Tang, L., Wang, H. and Li, G. (2013) ‘Advanced high strength steel springback optimization by projection-based heuristic global search algorithm’ Materials & Design Vol. 43, pp. 426-437.

Tekaslan, O., Gerger, N. and Seker, U. (2008) 'Determination of Spring-Back of Stainless Steel

Ling et al/ Journal of Scientific Research and Development, 3 (7) 2016, Pages: 91-97

97

Sheet Metal in V-Bending Dies', Materials and Design Vol. 29, pp. 1043-1050.

Tekiner, Z. (2003) 'An Experimental Study on the Examination of Springback of Sheet Metals with Several Thicknesses and Properties in Bending Dies', Journal of Materials Processing Technology Vol. 8, pp. 109-117.

Thipprakmas, S. and Phanitwong, W. (2011) 'Process Parameter Design of Spring-Back and

Spring-Go in V-Bending Process Using Taguchi Technique' Materials and Design Vol. 6, pp. 4430-4436.

Zhang, R. Y., Zhao, G. Y., Guo,,Z. H. and Quan, Y. P. (2015) ‘Effects of material parameters on springback of 5052 aluminium alloy sections with hat profile in rotary draw bending’, The International Journal of Advanced Manufacturing Technology, DOI 10.1007/s00170-015-7056-8