The processing maps (i.e. power dissipation maps superimposed over instability maps) can be used as a very convenient tool in case of an optimizing of hot forming processes. In this research, processing maps of C45 medium-carbon steel were assembled on the basis of an experimental flow stress dataset. This dataset was acquired via series of uniaxial hot compression tests in the temperature range of 1173 K - 1553 K and the strain rate range of 0.1 s~(-1) - 100 s~(-1). In addition, a predicted flow stress dataset was created with use of an artificial neural network approach - it allowed extending of the experimental dataset with additional temperature levels. The experimentally compiled processing maps have been subsequently enhanced by this additional dataset to encourage the overall information capability. The results have showed that the predicted dataset was useful to reveal additional instability regions in the experimentally assembled processing maps.
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