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Abstract. This paper presents a new parameter identification scheme for complex yield criteria of sheet metals using experimentally determined load and strain distributions and combining analytical stress analysis on sectional strain data with common inverse analysis. The approach is suitable for specimen with a non-homogenous strain distribution in the specimen and reduces computing time compared to common methods like iteratively updated FEM considerably. It serves to identify a set of material parameters in a fast and precise manner, describing the material behaviour.

Modeling sheet metal forming of materials for lightweight construction requires an understanding of their plastic behavior in different loading directions. The presented work focuses on twin‐roll‐casted magnesium alloy AZ31. It is characterized by unique mechanical properties compared to other magnesium alloys due to the employed twin‐roll‐casting‐process. In general, magnesium alloys with their hexagonal closed‐packed structure possess a complex forming behavior including a deformation‐induced anisotropy evolution. In the context of a fast design approach, an adaptation of the Yield2000‐2d criteria usually used for body‐centered cubic or face‐centered cubic materials is tested. The goal is a simple, versatile material model which parameters are determined just by tensile tests with moderate testing effort. In the investigated model, the yield locus definition is modified by adding a term for the yield exponent evolution during the forming process. The modeling approach is presented and the necessary tests for material data acquisition and evaluation are described. After experimental identification of the model parameters, the material model is applied in a forming simulation. The investigation provides promising results matching well with experimental data. Thus, the application of this model in a fast design step is feasible, offering valuable data like deformed shape, process‐related material properties and induced stresses for further processing.