Advanced High Strength Steel (AHSS) with high strength and deformation resistance is applied to automotive components and plays an important role in protecting passengers in the event of a crash, as well as contributing to fuel economy improvement by reducing the weight of the car body. However, due to the low ductility of the AHSS, there is an issue about the occurrence of fracture during a vehicle crash. In order to cope with these problems from the early design stage, preliminary verification is made through crash CAE analysis, but a high level of material property definition is required for fracture prediction. To predict fracture, many tests are required to secure the base data for parameter calculation of a complex fracture model, and a lot of physical time is required to verify the model. This paper aimed to semi-automate the material parameter calculation and verification process for efficient and reliable fracture prediction of AHSS. To this end, a user interface program was developed and its effectiveness was verified. The GISSMO fracture model in LS − DYNA® was used for fracture prediction, and 1.0GPa grade cold-rolled steel was examined. The existing method of calculating GISSMO parameters may have many error factors because it relies on the engineer's engineering judgement or the trial and error method. To reduce these error factors, LS − OPT® and LS − DYNA®, which are optimization tools, were linked to calculate and optimize parameters. Uniaxial tension, simple shear, notched tension and biaxial tension tests were conducted to evaluate the fracture characteristics of various load paths during crash events, and a drop weight impact test was performed for component-level verification. Finally, the validity of the method proposed in this study was reviewed by comparing the test and CAE analysis results.


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    Title :

    Efficient Processing of Material Property Definition to Predict Fracture of AHSS in Crash Analysis


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    WCX SAE World Congress Experience ; 2022



    Publication date :

    2022-03-29




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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