Improvement on CAE Model Considering Surface Deformation of Tools in Contact with Blank Sheet for Accurate Torsional Springback Prediction in High Strength Steel Part Forming
It is difficult to predict springback, particularly in torsion, with high accuracy by FE simulation. Generally, more accurate springback prediction havs been achieved mainly by the improvement of material modeling such as Baushinger effect and plastic anisotropy models. It is also proved that tool d...
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Veröffentlicht in: | Key engineering materials 2013-06, Vol.554-557, p.732-740 |
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Sprache: | eng |
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Zusammenfassung: | It is difficult to predict springback, particularly in torsion, with high accuracy by FE simulation. Generally, more accurate springback prediction havs been achieved mainly by the improvement of material modeling such as Baushinger effect and plastic anisotropy models. It is also proved that tool deformations can greatly influence on the accuracy of torsion springback prediction as shown in the authors’ study [Esaform 2012]. The study shows that FE simulation using elastic tool model has 30% more accuracy in predicting torsional spring back in a curved hat shape than that by rigid tool model. But full elastic tool modeling is tedious work and FE calculation with the elastic tool model needs enormous time.There are two kinds of tool deformation during a press forming: tool deflection as a whole, and surface deformation where the tool is in contact with the steel sheet. Three forming experiments were carried out with an insert block of different stiffness, which touch steel sheets directly, in this study. The results revealed that surface deformation of a tool has great influence on torsion springback of a curved hat shape. Based on the results, a new tool modeling is proposed in this study. In the model, the part of a tool in direct contact with a blank sheet is elastic and the other part is rigid. That means the model deals with only surface deformations of tools in FE simulation. By the new model, the accuracy of torsion springback prediction of a curved hat shape was improved with less calculation time. |
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ISSN: | 1013-9826 1662-9795 1662-9795 |
DOI: | 10.4028/www.scientific.net/KEM.554-557.732 |