Numerical Prediction and Reduction of Hat-Shaped Part Springback Made of Dual-Phase AHSS Steel

The springback in the sheet metal forming process refers to the change of shape after the load removal. It is usually undesirable, causing problems in the subsequent forming operations, in the assembly and negatively affects the quality of the final product. Numerical prediction of the springback with the use of the numerical simulation is crucial for the reduction of forming tool try-outs, reducing manufacturing costs and increasing the accuracy of the stamped part. In this work, numerical simulation was used for the springback prediction of the hat-shaped part made of advanced high-strength dual-phase steel HCT600X+Z. These numerical predictions were performed with the use of various combinations of material models to try to improve the prediction results. Furthermore, this work includes the proposed springback reduction measure. The reduction of the springback was achieved by the tool design which includes a counterpunch. The springback analysis was carried out in the side view of the formed part; the springback prediction results were compared with the experimental values.

[1]  A. Krasovskyy,et al.  Modeling of reverse loading effects including workhardening stagnation and early re-plastification , 2008 .

[2]  E. Evin,et al.  Physical Modelling and Numerical Simulation of the Deep Drawing Process of a Box-Shaped Product Focused on Material Limits Determination , 2019, Metals.

[3]  Naeini,et al.  Effects of Hardening Model and Variation of Elastic Modulus on Springback Prediction in Roll Forming , 2019, Metals.

[4]  Akitake Makinouchi,et al.  Simulation of springback and wrinkling in stamping of a dual phase steel rail-shaped part , 2006 .

[5]  Ismail,et al.  Effect of Elastic Module Degradation Measurement in Different Sizes of the Nonlinear Isotropic–Kinematic Yield Surface on Springback Prediction , 2019, Metals.

[6]  F. Ozturk,et al.  Formability and springback characterization of TRIP800 advanced high strength steel , 2012 .

[7]  M. Abspoel,et al.  A new method for predicting advanced yield criteria input parameters from mechanical properties , 2017 .

[8]  Byung-Min Kim,et al.  Effect of Constitutive Equations on Springback Prediction Accuracy in the TRIP1180 Cold Stamping , 2017 .

[9]  Guoxing Lu,et al.  Energy absorption of a bio-inspired honeycomb sandwich panel , 2019, Journal of Materials Science.

[10]  Lin Wang,et al.  The effect of yield criteria on the forming limit curve prediction and the deep drawing process simulation , 2006 .

[11]  Suwat Jirathearanat,et al.  Springback prediction in sheet metal forming of high strength steels , 2013 .

[12]  R. Hill.  A theory of the yielding and plastic flow of anisotropic metals , 1948, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[13]  Miklós Tisza,et al.  Formability Investigations of High-Strength Dual-Phase Steels , 2015, Acta Metallurgica Sinica (English Letters).

[14]  G. Lu,et al.  On the energy absorption of tube reinforced foam materials under quasi-static and dynamic compression , 2016 .

[15]  Ján Slota,et al.  Numerical Prediction of Forming Car Body Parts with Emphasis on Springback , 2018, Metals.

[16]  Ján Slota,et al.  Material Model of AW 5754 H11 Al Alloy for Numerical Simulation of Deep Drawing Process , 2016 .

[17]  Luís Menezes,et al.  Influence of boundary conditions on the prediction of springback and wrinkling in sheet metal forming , 2017 .

[18]  Hai Yu,et al.  Springback Reduction of L-Shaped Part Using Magnetic Pulse Forming , 2020, Metals.

[19]  M. Schikorra,et al.  Metal forming progress since 2000 , 2008 .

[20]  D. M. Neto,et al.  Influence of the plastic anisotropy modelling in the reverse deep drawing process simulation , 2014 .

[21]  Jun Bao,et al.  Effect of the material-hardening mode on the springback simulation accuracy of V-free bending , 2002 .

[22]  Guoxing Lu,et al.  High energy absorption efficiency of thin-walled conical corrugation tubes mimicking coconut tree configuration , 2018, International Journal of Mechanical Sciences.

[23]  Ján Slota,et al.  INFLUENCE OF TECHNOLOGICAL PARAMETERS ON THE SPRINGBACK ANGLE OF HIGH-STRENGTH STEELS , 2014 .

[24]  S.-D. Liu,et al.  Techniques to Improve Springback Prediction Accuracy Using Dynamic Explicit FEA Codes , 2002 .

[25]  Myoung-Gyu Lee,et al.  Advanced Issues in springback , 2013 .

[26]  F. Yoshida,et al.  Double-action bending for eliminating springback in hat-shaped bending of advanced high-strength steel sheet , 2020 .

[27]  W. L. Xu,et al.  Sensitive factors in springback simulation for sheet metal forming , 2004 .

[28]  F. Barlat,et al.  Plane stress yield function for aluminum alloy sheets—part 1: theory , 2003 .

[29]  Taylan Altan,et al.  Testing and modelling of material behaviour and formability in sheet metal forming , 2014 .

[30]  Ximing Li,et al.  Steel Microstructure Effect on Mechanical Properties and Corrosion Behavior of High Strength Low Carbon Steel , 2014, Metallurgical and Materials Transactions A.

[31]  N. Goo,et al.  A study of the thermal buckling behavior of a circular aluminum plate using the digital image correlation technique and finite element analysis , 2014 .