Numerical modeling based on digital material representation (DMR) prepared on the basis of the automated multiscale processing of microstructure photographs is the subject of this work. The main assumptions of the proposed algorithms, including image filtering, reconstruction, and grain analysis of one- and two-phase materials, are described in the paper. Their advantages and limitations are also discussed in detail. Then the results of the image analysis in the form of explicitly described microstructure representation in two dimensions are passed as input data directly to the finite element software. The sample of digital material is analyzed in order to generate the mesh of finite elements and to attach necessary rheological models. These data are then used during finite element simulations of plastometric compression tests. Examples of obtained stress and strain distribution across an explicitly represented microstructure after deformation are presented and discussed. Directions for future development of the presented approach are also highlighted.

Searching for a compromise between computing costs and predictive capabilities of metal processing models is the objective of this work. The justification of using multiscale and simplified models in simulations of manufacturing of DP steel products is discussed. Multiscale techniques are described and their applications to modeling annealing and stamping are shown. This approach is costly and should be used in specific applications only. Models based on the JMAK equation are an alternative. Physical simulations of the continuous annealing were conducted for validation of the models. An analysis of the computing time and predictive capabilities of the models allowed to conclude that the modified JMAK equation gives good results as far as prediction of volume fractions after annealing is needed. Contrary, a multiscale model is needed to analyze the distributions of strains in the ferritic-martensitic microstructure. The idea of simplification of multiscale models is presented, as well.

Maintaining the temperature of liquid steel in the ladle in the required range affects the quality of casted billets, reduces energy consumption, and guarantees smooth control of the melting sequence. Measuring its temperature is a challenging task in industrial settings, often hindered by safety concerns and the expensive nature of equipment. This paper presents models which enable the prediction of the cooling rate of liquid steel for variable production parameters, i.e., steel grade and weight of melt. The models were based on the FEM solution of the Fourier equation, and machine learning approaches such as decision trees, linear regression, and artificial neural networks are utilized. The parameters of the model were identified using data from the monitoring system and inverse analysis. The results of simulations were verified with measurements performed in the production line.