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

Won, Chan-Hee; Lee, Wonju; Lee, Hyoun-Young; Kang, Yeonsik; Yoon, Jonghun

doi:10.1007/s00170-020-05537-x

Cited by 9 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Figure 14(b) demonstrates the prediction results using HE and SET test datasets that the overestimation and underestimation problems are solved with representing exhibiting a 1.5% of HER difference. It is evaluated that training efficiency can be improved due to the similar enhanced mechanism of sheared edge stretchability although shearing method and stretching direction are different [8,9].…”

Section: Predicted Results Of Hole Expansion Ratiomentioning

confidence: 99%

“…Figure 2 demonstrates the engineering strain-stress curves of two different GPa-grade steels with XF980 and TRIP1180. TRIP1180 is one of the conventional GPa-grade steels which is widely applied in BIW panels, and XF980 is categorized as a third-generation advanced high-strength steel exhibiting a good strengthductility combination [8][9].…”

Section: A Materials Testing Datamentioning

confidence: 99%

“…However, it is not proper to apply for the advanced punching method such as humped bottom punch and twostage punching which show remarkable improvements in HER compared to the flat punch, since advanced punching methods can improve the HER by dramatically reducing the hardness at the bottom of fracture zone, although the maximum hardness is similar to the conventional punch as shown in Fig. 5 [8][9].…”

Section: Materials Hardening Datamentioning

confidence: 99%

“…An ANN algorithm is applied to predict the edge stretchability of GPa-grade steels for various material hardening and punching conditions with 72 experimental data set. Additional 216 data-sets of SET test, which is one of edge stretchability methods by tensioning the tensile specimen with sheared induced on one side of edge, is applied to increase the training efficiency since the SET test is also affected by the material properties and material hardening in terms of strain hardening exponent and material hardening profile [8][9]. In a single data set, 23 input features for representing three parameters as listed in Table 2, which are corresponded with the output feature of the pre-damage strain.…”

Section: A Artificial Neural Network(ann) Modelmentioning

confidence: 99%

“…It is difficult to predict edge cracking using numerical simulations with conventional forming limit curves since the onset of edge cracking occurs at a significantly lower strain than that of the forming limit curves, which causes significant delays in new product development [5][6]. Moreover, edge cracking is still being reported in the third-generation advanced high-strength steels which have been recently developed to obtain superior elongation while maintaining inherent strength [7][8][9]. For these reasons, the prediction of edge cracking is an enormous challenge for the wide application of GPa-grade steels in BIW structures.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Predicted Results Of Hole Expansion Ratiomentioning

confidence: 99%

Section: A Materials Testing Datamentioning

confidence: 99%

Section: Materials Hardening Datamentioning

confidence: 99%

Section: A Artificial Neural Network(ann) Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

2020

Self Cite

View full text Add to dashboard Cite

show abstract

Prevention of delayed cracking of punched 1.5 GPa ultra-high strength steel sheets by ironing with punched slug

Mori

Abe

Murai

2020

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Hydrogen-induced delayed cracking of punched 1.5 GPa ultra-high strength steel sheets was prevented by ironing with a slug ejected from punching, because the risk of delayed cracking for sheared 1.5 GPa sheets is very high due to high tensile residual stress and large plastic deformation. To prevent the occurrence of delayed cracking, the sheet was punched, and then, the punched hole was ironed by passing the punched slug. Although the residual stress around the punched edge of the 1.5 GPa sheet was tensile, the stress was turned to compressive stress by slug ironing. In addition, the fracture surface of the sheared edge was changed to a smooth ironed surface. A cathode hydrogen charging test of the punched edge for delayed cracking was performed. Although delayed cracks were caused at the punched hole by hydrogen charging, no cracks occurred at the ironed hole for charging. It was found that slug ironing of the punched hole is effective in preventing delayed cracking.

show abstract

Effect of two-stage press blanking on edge stretchability with third-generation advanced high-strength steels

Won

Lee

et al. 2020

Int J Adv Manuf Technol

View full text Add to dashboard Cite

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

Cited by 9 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

Prevention of delayed cracking of punched 1.5 GPa ultra-high strength steel sheets by ironing with punched slug

Effect of two-stage press blanking on edge stretchability with third-generation advanced high-strength steels

Contact Info

Product

Resources

About