Extension of experimentally assembled processing maps of 10CrMo9-10 steel via a predicted dataset and the influence on overall informative possibilities

Abstract

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.