Effect of Springback on A6061 Sheet Metal Bending: A Review

Cinar, Zeki; Asmael, Mohammed; Zeeshan, Qasim; Safaei, Babak

doi:10.17576/jkukm-2021-33(1)-02

Cited by 17 publications

(6 citation statements)

References 41 publications

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“…Several studies investigated the effects of increasing yield strength on the same material subjected to different degrees of heat treatments of aluminum sheets ,such as AA 2014 (O, T4, and T6) and AA3003 (O, H22, and H24).They reported the highest SB in the T6 and H24 tempers and the least SB in the conditions yield strength is increased [45,46].A large punch-holding time or bottoming minimizes SB [32,36,38].SB and bending load decreases with increasing frequency, RMS, and peak current density because of the electroplastic effects of electric pulse during electricity-assisted forming.Electric pulse-assisted forming is less energy intensive compared with traditional warm forming processes [47][48][49][50][51]. Highly precise dimensions of the product in sheet metal-forming process are required to obtain a precise work piece.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Springback Behavior of Vee Bending Process of AA5052 Aluminum Alloy Sheets Using Machine Learning

2023

jjmie

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This study explores the effect of Vee bending process parameter on the springback (SB) behavior of aluminum (AA5052) alloy at sheet thickness of (2 and 3mm) with die-opening (22, 35, and 50 mm) and punch-holding time (0, 5 and 10 second) which were experimentally examined. Furthermore, to see the relative effect of process parameter on SB behavior, a qualitative approach of analysis of variance (ANOVA) was used, whereas multi linear regression (MLR) and artificial neural network (ANN) were applied to optimize the SB behavior on specified process parameters. The experimental results revealed that as punch holding time and sheet thickness increase, SB behavior reduced, whereas in case of die opening, opposite phenomena observed. ANOVA results revealed that punch-holding time had the greatest effect on SB, followed by die opening and sheet thickness. Two-way parametric interactional effects between punch-holding time and dieopening had a significant effect on SB behavior. By contrast, the interactional effects of sheet thickness were insignificant. The comparative study of MLR and ANN shows that The ANN has better (99% SB predictability) as compared to MLR (73% SB Predictability). Furthermore, the predicted results of both models were compared with actual experimental results. It was observed that the predicted results were approximately near with actual measurements, whereas the performance of MLR and ANN model were measured from sum of absolute error and the sum of the absolute error of ANN was about 12% of that of MLR model. Therefore, ANN produced a superior SB prediction performance compared with MLR. This work demonstrates the formability of AA5052 aluminum alloy in cold work where Vee bending was performed with a punch radius of 0.8 mm.The bend specimens showed no cracks, checking, and surface roughness.

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Section: Introductionmentioning

confidence: 99%

Prediction of Springback Behavior of Vee Bending Process of AA5052 Aluminum Alloy Sheets Using Machine Learning

2023

jjmie

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“…The springback in the cold plastic forming of the sheet metals is given by the difference between the final shape of the part and the shape obtained at the end of the punch stroke. After the withdrawal of the tools, the formed part undergoes significant changes in shape, which means that springback is an additional material deformation that occurs during rebound [ 6 , 7 , 8 ], as indicated in Figure 1 . The analysis of stress/strain diagrams leads to the conclusion that the intensity of springback will increase with the material strength [ 9 , 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Because the phenomenon of springback is favored by the material’s elastic properties, any factor that decreases the ratio between elastic and plastic strain determines the reduction in the springback intensity. Therefore, materials with a lower yield strength and Young’s modulus, higher hardening, a normal anisotropy index, a higher clamping force value, a different punch geometry, or a lubricated environment between the die and the sheet metals can reduce the intensity of the phenomenon [ 1 , 2 , 6 , 7 , 8 , 9 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the tools specific to the plastic forming process can be man specific way to satisfy the requirements to decrease the intensity of the spr The rebound process is elastic, but a secondary plastic flow can also occur due to the bending and straightening around the edges of the die and punch. When metal sheets are bent, springback is manifested by the angle change and the formed part's curvature [1,2,[6][7][8]. The parameters that characterize the effect of springback are the springback angle θ and the radius of curvature R after the rebound.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Algorithm and Additively Manufactured Punch Used to Form Aluminum Sheet Metal Parts

et al. 2023

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Self-adaptive mechanisms are gaining momentum in industrial processes. It is understandable that as the complexity increases, the human work must be augmented. Considering this, the authors have developed one such solution for the punch-forming process, using additive manufacturing, i.e., a 3D-printed punch, to draw into shape 6061-T6 aluminum sheets. This paper aims to highlight the topological study used to optimize the punch form shape, the methodology of the 3D printing process, and the material used. For the adaptive algorithm, a complex Python-to-C++ bridge was created. It was necessary as the script has computer vision (used for calculating stroke and speed), punch force, and hydraulic pressure measurement capabilities. The algorithm uses the input data to control its subsequent actions. Two approaches are used in this experimental paper, a pre-programmed direction and an adaptive one, for comparison purposes. The results, namely the drawing radius and flange angle, were statistically analyzed using the ANOVA methodology for significance. The results indicate significant improvements when using the adaptive algorithm.

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“…The bending process consists of several types: V-bending, U-bending, L-bending, and Z-bending [4]- [7]. However, one type of bending commonly used is V-bending [8]. The bending process consists of several types: V-bending, U-bending, L-bending, and Z-bending [9].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Springback Phenomenon of the Material Aluminum Alloy 6063-T5 With Punch Angle Variation

Abdulah

Kusnadi

Rizkiyanto

et al. 2022

JTMMX

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Springback is a phenomenon that frequently occurs in bending processes and is detrimental to the manufacturing industry. Typical disadvantages include the incompatibility of the product's final dimensions and the need for additional processing. Indeed, these must also incur additional expenses and result in the inefficient production process flow. Consequently, the analysis of springback is attracted to studies to obtain a bending product that meets expectations. This study examined the springback phenomena on 60 mm x 19 mm x 2 mm aluminium alloy 6063-T5 materials using V-bending processes and approximately 40°, 50°, and 60° punch angles. This study also utilized the die opening and bending force variables, 35 mm and 2500 N, respectively. It was anticipated that these procedures would produce a 6063-T5 aluminium alloy with a bending angle of approximately 90 degrees. The results indicate that the bending angles produced by these processes with the various punch angles are 91.50°, 91.42°, and 91.67°, respectively. It indicates that the excess bending angle was referred to as springback. Consequently, a bending angle of approximately 90° can be obtained on aluminium alloy 6063-T5 using V-bending processes with the set parameters of die opening in 35 mm and bending force in 2500 N, in addition to the punch angle that was reduced based on the known springback value. In addition, it is anticipated that this result will contribute to the development of the manufacturing industry, particularly the aluminium alloy 6063-T5 materials industry, or serve as a reference for other relevant studies

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Effect of Springback on A6061 Sheet Metal Bending: A Review

Cited by 17 publications

References 41 publications

Prediction of Springback Behavior of Vee Bending Process of AA5052 Aluminum Alloy Sheets Using Machine Learning

Prediction of Springback Behavior of Vee Bending Process of AA5052 Aluminum Alloy Sheets Using Machine Learning

An Adaptive Algorithm and Additively Manufactured Punch Used to Form Aluminum Sheet Metal Parts

An Analysis of the Springback Phenomenon of the Material Aluminum Alloy 6063-T5 With Punch Angle Variation

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