The Evaluation of Process Parameters on Springback in V-bending Using the Flexforming Process

Karaağaç, İbrahim

doi:10.1590/1980-5373-mr-2016-0799

Cited by 11 publications

(8 citation statements)

References 16 publications

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“…The internal stresses from bending were relieved, thereby decreasing the elastic strains in the bending zone and increasing the permanent (plastic) strains. Moreover, the bending moment due to compressive load acting normally to the sheet decreased [54,55].…”

Section: Resultsmentioning

confidence: 97%

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: Resultsmentioning

confidence: 97%

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

2023

jjmie

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“…As the thickness reduced due to strain, hardness will increase. Furthermore, strain also played a significant role in the springback results where the increasing hardening was due to strain and will increase the springback [23,30,31]. Figure 11 shows the strain hardening index, n versus the precentage of springback taking the minimum thickness, t as the parameter to be studied.…”

Section: Resultsmentioning

confidence: 99%

Study of springback pattern of non-uniform thickness section based on V-bending experiment

Adnan¹,

Abdullah²,

Samad³

2017

J. MECH. ENG. SCI.

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Non-uniform thickness section is one of most effective approaches in reducing automotive part weight. Less weight results in less fuel consumption and therefore good for the environment. Formability is the main issue of non-uniform thickness section, since changes in thickness may cause parts to behave distinctively compared to uniform thickness during the forming process. One of the main concerns is the springback pattern. This study focused on springback behaviour of AA6061 strip with non-uniform thickness through V-bending experiment. The outcome will contribute in the manufacturing of automotive parts with non-unifrom thickness, where defects such as springback can be predicted and minimised. In the experiment, three parameters i.e. thickness ratio, bend radius and alignment were studied and the effects were observed. The result found that thickness was the most influential parameters compared to others. Thickness was reduced through cold deformation, causing the hardness to increase and then resulted in higher springback value. The effect of alignment was almost negligible. Validation made based on the result found is in agreement with prior published works.

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“…This unbalanced stress distribution on the part is the main factor that creates springback [24]. Increasing the die angle increases the bending moment.…”

Section: Experimental and Fe Analysesmentioning

confidence: 99%

Experimental, Statistical, and Analytical Evaluation of The Springback Behavior of Martensitic 1400 Sheet in V-Bending

N¹,

Seçgin²

2020

Preprint

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Ultra-high-strength Martensitic 1400 steel is preferred in the automotive industry because of its high strength as well as its light weight. In this study, Martensitic 1400 steel was subjected to the V-bending process. As a result of 48 different tests, the amount of springback resulting from the V-bending process was determined. A finite element (FE) model was then created based on these experimental data. After it was determined that the experimental results concurred with the FE model, without performing new experiments, further analyses were carried out at different temperatures. Using the results of a total of 96 FE analyses, variance analysis was performed and the effects of the operation parameters on springback were determined. As a result of the study, it was concluded that the most effective parameter on springback in the V-bending process was the die angle and that holding time had no significant effect. It was observed that the springback increased in parallel with the punch radius and die angle and that increases in temperature reduced the springback.

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The Evaluation of Process Parameters on Springback in V-bending Using the Flexforming Process

Cited by 11 publications

References 16 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

Study of springback pattern of non-uniform thickness section based on V-bending experiment

Experimental, Statistical, and Analytical Evaluation of The Springback Behavior of Martensitic 1400 Sheet in V-Bending

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