Minimizing wrinkling formation of GPa-grade steels in multi-stage crash forming process

Won, Chan-Hee; Kim, Dong-Jin; Yoon, Jonghun

doi:10.1007/s00170-019-04419-1

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…GPa-grade steels which exhibit an ultimate tensile strength of at least 1,000 MPa have been widely applied to body-inwhite (BIW) structures to improve their crash worthiness and reduce their weight [1][2][3]. However, undesired fractures frequently occur during the stamping and flanging process due to the poor formability and sheared edge stretchability of GPa-grade steels, which restricts their wide application in BIW panels [4][5].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Predicting Edge Stretchability in Hole Expansion Test With Gpa-Grade Steel

2020

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This paper mainly proposes an artificial neural network (ANN) model for predicting edge stretchability of GPa-grade steels, which is substantially difficult to predict due to the complex nonlinear relation among the numerous sheared edge qualities. We newly suggest the physically characterized parameters, such as material properties, deformed shape, and work hardening of sheared edge, to predict the various materials and punching methods, simultaneously. The proposed parameters are trained with the predamage strain which is calculated by inherent fracture strain and experimental results in terms of hole expansion ratio. To prevent the overfitting issues, cross validation method with additional datasets from a different kind of edge stretchability test such as sheared edge tensioning test are utilized. Experimental validations have been conducted with various GPa-grade steels and sheared edge conditions, which are compared with the proposed ANN model and numerical simulation. The proposed ANN model exhibits remarkable performance in the prediction of hole expansion ratio having a mean absolute error of 1.5% when compared to the previous studies such as numerical simulation and ANN model with utilizing the maximum hardness measured at the sheared edge. INDEX TERMS Artificial neural network, edge cracking, edge stretchability, GPa-grade steels, sheared edge quality.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Predicting Edge Stretchability in Hole Expansion Test With Gpa-Grade Steel

2020

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“…Workers could not avoid having to undertake much trial and error to find the optimized motion law [10]. Chanhee et al [11,12] carried out experimental validations by applying the design variables suggested by the safe forming window for the multi-stage forming.…”

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confidence: 99%

Sheet Metal Forming Optimization Methodology for Servo Press Process Control Improvement

Prete

Primo

2020

Metals

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In sheet metal forming manufacturing operations the use of servo presses is gaining more interest due to the opportunity to improve process performance (quality, productivity, cost reduction, etc.). It is not yet clear how to proceed in the engineering process when this type of operating machine is used to achieve the maximum possible potential of this technology. Recently, several press builders have developed gap- and straight-sided metal forming presses adopting the mechanical servo-drive technology. The mechanical servo-drive press offers the flexibility of a hydraulic press with the speed, accuracy and reliability of a mechanical press. Servo drive presses give the opportunity to improve the productivity of process conditions and improve the quality of stamped parts. Forming simulation and numerical optimization can be useful tools to define beforehand the optimal process parameter set-up in terms of servo press downward curve properties. This is done by carrying out a sensitivity analysis of the forming parameters having influence on said curve. The authors have developed a numerical methodology able to analyze the influence factors, for comparison with the degrees of freedom made available by the usage of a servo press, in terms of stroke profile management, to obtain an optimized process parameters combination.

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Evaluation of in-plane edge stretchability under severe contact condition for third-generation advanced high-strength steel

Won

Lee

et al. 2020

Int J Adv Manuf Technol

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Minimizing wrinkling formation of GPa-grade steels in multi-stage crash forming process

Cited by 5 publications

References 22 publications

Artificial Neural Network for Predicting Edge Stretchability in Hole Expansion Test With Gpa-Grade Steel

Artificial Neural Network for Predicting Edge Stretchability in Hole Expansion Test With Gpa-Grade Steel

Sheet Metal Forming Optimization Methodology for Servo Press Process Control Improvement

Evaluation of in-plane edge stretchability under severe contact condition for third-generation advanced high-strength steel

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