Experimental and numerical prediction of springback in V-bending of anisotropic sheet metals for automotive industry

Slota, Ján; Jurčišin, Miroslav

doi:10.7862/rm.2012.5

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…This was accomplished for steel sheet metal of high tensile strength by modelling its kinetic friction. FEM was also used by Slota and Jurcisin to predict the springback response for steel sheet metals in an air bending process [14]. Another FEM prediction model was established by Choi et al to investigate the springback response of high tensile steels after a U bending process.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Springback using the Machine Learning Technique in high-tensile strength sheet metal during the V-Bending Process

2023

jjmie

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Bending is one of the widely used forming processes for sheet metals. However, due to the metal elasticity, the springback characteristic is unavoidable, leading to deviations from the desired final shapes and causing cumulative fitting problems in the assembly stages. Thus, precise predictions of the springback responses will enhance the sheet metal forming and the overall manufacturing processes. This is achieved by employing tree-based machine learning algorithms. These algorithms are used for their simplicity, preciseness, and consistency. Based on the tree-based algorithms, many prediction models are constructed and evaluated. First, experimental setup is established to measure the springback angles for different manufacturing conditions such as: the bending angle, the sheet metal's width and thickness, the machine settings, etc. Then, these data sets are divided into training and testing groups for the prediction models. This division is carried randomly, where 90 % of the data sets are used for training, and 10 % are left for testing the models' accuracy. The models are evaluated by comparing their predicted springback angles with the experimental values. The deviation errors are measured using the Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE).It isrevealed that the LightGBM prediction model is the most accurate model with 0.42 deg., 0.26 deg., and 0.52 deg. for MAE, MSE, and RMSE, respectively. The Gradient boosting comes in the second place with 0.66 deg., 0.760 deg., and 0.80 deg. for MAE, MSE, and RMSE, respectively.

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Section: Introductionmentioning

confidence: 99%

Prediction of Springback using the Machine Learning Technique in high-tensile strength sheet metal during the V-Bending Process

2023

jjmie

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“…Numerical simulation of forming can help in solving technological problems, such as stamping failure, wrinkling creation and springback [Spisak 2000, Mulidran 2018]. The accuracy, reliability of the prediction of the deformation behavior of the material by numerical simulation depends on the nonlinearity of the material properties, on the defined critical values of deformations, friction ratios on the contact surfaces between sheet and tool, and on the anisotropy in the sheet thickness and sheet plane [Slota 2012, Krenicky 2012, Tomas 2019, Brusilova 2020. In this work, the influence of surface anisotropy and friction conditions on the drawing process of the gutter end cap (Fig.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Forming Process of Gutter End Cap Using the Finite Element Method

Mulidrán¹,

Spišák²,

Majerníková³

et al. 2021

MM SJ

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The contribution deals with the optimization of the forming process with the use of FE analysis. The impact of the planar anisotropy and friction coefficient on the drawing process was evaluated in the numerical simulation. Optimization of metal blank size and shape with the use of metal forming simulation was also performed. Studied material was galvanized drawing quality steel which is used for the production of the rain gutter end cap. Effects of planar anisotropy and friction coefficient on the quality of steel stamping were evaluated with the use of FE simulation. The effect of anisotropy was also experimentally tested. The aim of this work was to determine the correct steel blank size and shape and to evaluate the effects of planar anisotropy on the thickness variation and wall wrinkling.

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“…Ramezani et al (2010) used the FEM approach and related the kinetic friction with the springback during the V-bending process of ultra-high tensile steels. Slota and Jurcisin present another springback prediction model for the TRIP, AHSS and mild Steel during the air-bending process (Slota and Jurcisin, 2012). A comparative analysis for the springback is presented in Da Silva et al This research differentiates the behaviour in springback in advanced high-tensile strength material sheet-metals (DP600 and DP780) during the air-bending process (Da Silva et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Analysis and Optimization of Springback during the V-bending of Hot-Rolled High Strength Steels (JSH440)

Wasif

Fatima

Iqbal

et al. 2021

JER is an international, peer-reviewed journal that publishes f

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Influences of thickness, width, bend angle, applied load and holding time are evaluated over the springback in hot-rolled, high-tensile strength sheet-metals (JSH-440). Blanks' thickness, width and bend angles are considered the geometric parameters, whereas applied load and holding time are the process parameters considered in the research. Analysis of variance (ANOVA) and sensitivity analysis are applied to evaluate the significance of the factors over the springback magnitude. Analytical models are developed to predict the springback in the sheet metals for the desired geometric and process parameters. Simplified analytical models are also developed for different geometries of sheet metals. Finally, the Genetic Algorithm has also been applied to determine the optimal process parameters for the minimum springback with varying geometries of the sheet metals. Finally, the influence of parameters and optimized results are discussed in detail.

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Experimental and numerical prediction of springback in V-bending of anisotropic sheet metals for automotive industry

Cited by 5 publications

References 16 publications

Prediction of Springback using the Machine Learning Technique in high-tensile strength sheet metal during the V-Bending Process

Prediction of Springback using the Machine Learning Technique in high-tensile strength sheet metal during the V-Bending Process

Optimization of the Forming Process of Gutter End Cap Using the Finite Element Method

Analysis and Optimization of Springback during the V-bending of Hot-Rolled High Strength Steels (JSH440)

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