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Forming Limit Curves of Advanced High Strength Steels: Experimental Determination and Empirical Prediction
ISSN: 1946-3979, e-ISSN: 1946-3987
Published April 03, 2018 by SAE International in United States
Citation: Huang, L. and Shi, M., "Forming Limit Curves of Advanced High Strength Steels: Experimental Determination and Empirical Prediction," SAE Int. J. Mater. Manf. 11(4):409-418, 2018, https://doi.org/10.4271/2018-01-0804.
For the past decades, the adoption of empirical equations in the forming limit curve (FLC) calculation for conventional steels has greatly simplified the forming severity assessment in both forming simulations and on the stamping shop floor. Keeler’s equation based on the n-value and sheet thickness is the most popular one used in North America. However, challenges have been encountered on the validity of the equation for advanced high strength steels (AHSS) since Keeler’s equation was developed based on the FLC data mostly from mild steels and conventional high strength steels. In this study, forming limits of various AHSS grades under different strain conditions are experimentally determined using digital image correlation technique. Both Marciniak cup and Nakazima dome tests are exercised to demonstrate the differences in the resultant forming limits determined with different test methods. The effects of a few material-related aspects on the FLC of AHSS are elaborated, including (1) microstructures (C-Mn, dual phase and retained austenite containing steels), (2) material thickness (0.8 to 2.3 mm), and (3) coating process. Pertaining to these effects, the effectiveness of Keeler’s equation in the FLC estimation for various AHSS grades is evaluated with experimental FLC data. It has been illustrated that with proper modifications and compensations, the conventional empirical equations remain as useful references to approximate the FLC of AHSS. General recommendations and guidelines are provided regarding the practical applications of FLCs of AHSS grades. In the meantime, further development of FLC models is initiated and discussed based on statistical analysis to achieve improved reliability.