Processing maps embody a supportive tool for the optimization of hot forming processes. In the present work, based on the dynamic material model, the processing maps of 10CrMo9-10 low-alloy steel were assembled with the use of two flow curve datasets. The first one was obtained on the basis of uniaxial hot compression tests in a temperature range of 1073–1523 K and a strain rate range of 0.1–100 s−1. This experimental dataset was subsequently approximated by means of an artificial neural network approach. Based on this approximation, the second dataset was calculated. An important finding was that the additional dataset contributed significantly to improving the informative ability of the assembled processing maps in terms of revealing potentially inappropriate forming conditions.

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In Železiarne Podbrezová, cold drawing process is the final process in production of precision seamless steel tubes. This particular technology utilizes multiple drawing sequences and intermediate annealing. From the physical point of view, it is nothing just the optimal use of plastic deformation during cold forming that grants the final tube dimensions. The drawing process itself is significantly affected by physical and metallurgical properties of the tube, the tool geometry, the lubrication, and the sequence of operations. This paper deals with the relationship between the tool geometry and the drawing force. The FEM-based numerical model of the process was prepared in DEFORM 3D in order to optimize the geometry of the die; eight die geometries were investigated in total. The numerical simulation itself considered a hot rolled hollow at Ø32 mm × 4 mm, cold drawn into Ø25 mm × 4 mm using die drawing (sinking) sequences only. Calculated drawing force showed that the change of the run-in angle of the die led to a decrease of the drawing force.

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