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1 2015

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Materials modelling in industrial bulk metal forming processes and process chains

Hirt G., Bambach M., Lohmar J., Guvenc O., Henke T., Schwich G.

Computer Methods in Materials Science

2015

Vol. 15, No.1

5--12

Bulk metal forming processes range from processes with a single deformation step such as certain closed-die forging operations to processes with many subsequent stages such as hot rolling, ring rolling or open die forging. Modelling of these manufacturing processes requires both precise process models as well as adequate material models. Microstructure evolution by recrystallization is decisive in all of these processes since the microstructure determines the flow stress and hence the forming forces but it also influences the product properties. In this context, the propagation of variations in the processing conditions and in the material behavior are of special importance and methods for the quantification of uncertainties and their effect on model predictions are required. Such questions can be approached using models of different complexity on various scales as shown in the following examples: In closed die forging of a gear wheel from 25MoCr4 alloy the complex geometry requires a Finite Element process model which in this case is combined with a JMAK type material model. In plate rolling a simplified process model can be applied successfully. Based on the slab theory, which is enhanced for spatial resolution of shear strain using a meta model derived by FEM, this model can simulate even longer roll pass schedules within seconds and offers the possibility to combine it with numerical optimization techniques. Recrystallization of a high-manganese steel in interpass times between hot rolling passes is an example where models with spatial resolution (CP-FEM and phase field) are combined on the micro-scale to predict the recrystallization kinetics based on physically meaningful variables such as grain boundary mobility. In ring rolling the process model must include the closed-loop control system of the rolling machine to achieve a realistic prediction of the process kinematics. Feedback control loops for up to eight kinematic degrees of freedom (velocities and positions of all radial, axial and guiding rolls) have been defined using virtual sensors integrated in the simulation. Offline coupling with microstructure simulation is used to predict the final grain size and determine under which conditions static recrystallization occurs during the rolling sequence.