Methodological approach for the texture deformation analysis in the cold extrusion process

Abstract

© 2017, Springer-Verlag London. Solving problems of stress-strain analysis in plastic deformation processes can be carried out by many methods. Almost all solutions can be reduced to an analysis of theoretical and experimental results, obtained by some mathematical models as well as by experimental testing of the plastic deforming process. The ultimate goal is to determine the stress-strain state, the field of strain rate, and deformation velocity within the volume of the material that is being plastically deformed. The aim of this investigation is to determine the parameters given earlier by tracing the grain structure microdeformations of a low carbon steel in a meridian cross section at ambient temperature by means of an artificial neural network (ANN) during bulk forming. The three key parameters selected for the description of the deformed microstructure are the angle of rotation and the major and the minor axes of the ideal grain, which are used to represent the plastic deformation in a selected point. The ideal grain model, for the selected point of the meridian cross section, represents the plastic deformation in line with the selected parameters for the defined number of ferrite grains. The ANN models were developed using steel microstructure data, which were obtained experimentally by using three experimental tool dies for forward extrusion. Their verification was carried out on three different angles of extrusion. This method yielded the size of the plastic deformation of the grain structure in the meridian cross section during forward extrusion, which can serve as a basis for further stress-strain analysis.