Experimental investigations and multiscale modeling of the microstructure evolution and the mechanical properties of a ferritic steel grade during the production process

Helm, Dirk; Baiker, Maria; Bienger, Pierre
May 2013
AIP Conference Proceedings;May2013, Vol. 1532 Issue 1, p197
Academic Journal
The process chain for sheet metals after casting to produce components made of semi-finished products is complex and the resulting mechanical properties of the produced material depend strongly on the evolution of the microstructure. After casting, a typical process chain consists of hot rolling, cold rolling, annealing, skin pass rolling, and sheet metal forming. In order to represent the microstructure evolution in an adequate way, a multiscale modeling concept is applied for the process steps cold rolling, annealing, and sheet metal forming. In this Integrated Computational Materials Engineering (ICME) concept, the strong microstructure evolution during the production of semi-finished products is modeled by using crystal plasticity for the representation of the cold rolling process and a cellular automaton is incorporated to model the annealing procedure. In both cases, only the microstructure in an adequate unit cell is considered. For sheet metal forming, the whole component has to be simulated together with the interaction between workpiece and the forming tools in order to solve technological problems like springback. For this purpose, classical macroscopic plasticity models have been applied. To connect the different length scales of the modeling approaches, a scale transition on the basis of numerical homogenization is introduced for the determination of the mechanical properties like the multi-axial yield behavior. These information are required to virtually determine the type of the macroscopic plasticity model, the material parameters of the plasticity model, and to simulate sheet metal forming processes. In the article, the different modeling approaches are compared step by step with experimental investigations in order to prove the predictability of each modeling technique.


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