Abstract
Ultra-high-strength steels (UHSS), as one of the difficult-to-form building materials, pose more challenges in accurate springback prediction of cold-bending design, which are attributed to the high yield stress, the uncertainty of hardening response and the varying chord modulus. A total of 11 specimens made of Q960 UHSS involving the cyclic loading-unloading-reloading tensile test and the springback followed by three-point bending test, were designed to investigate the degeneration behavior of chord modulus and the springback response. Subsequently, a constitutive model set, including the Voce isotropic hardening model, the updated Voce-Chaboche combined hardening model with modified continuum damage mechanics model, and the aforementioned combined hardening model with Yoshida chord modulus model, was applied in the springback analysis with the user-defined material subroutine VUMAT. The verification results indicate that the type of hardening model (isotropic or kinematic) has negligible influence on springback, while the steels with higher strength and more degeneration of chord modulus have more amount of springback. Finally, with a database consisting of 365 data lists built up, the XGBoost machine learning model was utilized and verified in terms of the capacity of predictability for the bending and springback performance with different geometrical parameters, providing a basis about the feasibility design and mould design of cold bending for Q960 UHSS.
| Original language | English |
|---|---|
| Article number | 112934 |
| Journal | Thin-Walled Structures |
| Volume | 209 |
| DOIs | |
| State | Published - Apr 2025 |
Keywords
- Chord modulus
- Q960
- Springback
- Ultra-high-strength steel
- XGBoost algorithm
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