Continuous development of the automotive industry is associated with the search for construction materials that combine high strength with good plastic properties and which allow improvement of the process technology. DP steels meet the high requirements for materials currently used in the automotive industry. Production of DP steels is a very complex process requiring precise control of technological parameters during thermo-mechanical treatment. Design of these processes can be significantly improved by the numerical models of phase transformations occurring in the DP steels. The main aim of this work is multiscale modeling of the austenite decomposition into ferrite, bainite, and martensite in processes of laminar cooling. Partial differential diffusion equation of carbon diffusion is solved with a moving boundary (Stefan problem). The solution was performed in the real microstructure of austenite, which was obtained using the electron microscope image and digital material representation. The developed model based on finite element modeling (FEM) solution of a diffusion equation allows one to determine phase volume fractions, grain size, and carbon segregation before the front of transformation in fluctuating temperature conditions. Results of numerical simulations were used for development of the relationship between microstructure and mechanical properties of DP steel strips.

The mechanical behaviours of microalloyed and low-carbon steels under strain reversal were modelled based on the average dislocation density taking into account its allocation between the cell walls and cell interiors. The proposed model reflects the effects of the dislocations displacement, generation of new dislocations and their annihilation during the metal-forming processes. The back stress is assumed as one of the internal variables. The value of the initial dislocation density was calculated using two different computational methods, i.e. the first one based on the dislocation density tensor and the second one based on the strain gradient model. The proposed methods of calculating the dislocation density were subjected to a comparative analysis. For the microstructural analysis, the high-resolution electron backscatter diffraction (EBSD) microscopy was utilized. The calculation results were compared with the results of forward/reverse torsion tests. As a result, good effectiveness of the applied computational methodology was demonstrated. Finally, the analysis of dislocation distributions as an effect of the strain path change was performed.

The coupled finite element multiscale simulations (FE2) require costly numerical procedures in both macro and micro scales. Exertions, leading to improvement of numerical efficiency, are focused mainly on two areas of development i.e. parallelization of numerical procedures or simplification of virtual material representation. One of the representatives of the latter area is the idea of Statistically Similar Representative Volume Element (SSRVE). It aims at the reduction of the number of finite elements in micro scale by transformation of sophisticated images of material microstructure into artificially created simple objects being characterized by similar features as their original equivalents. This paper is devoted to analysis of shape coefficients and possibilities of their application in the creation of SSRVE for DP steels. The results from sensitivity analysis of particular coefficients, obtained for different microstructure images, are presented in the paper, as well as examples of SSRVE.

The paper describes the material database, which was developed and included in the VirtRoll computer system dedicated to the design of optimal hot strip rolling technologies. The structure and functionalities of the database are described in the first part of the paper. The integration between the database and the system through the Scalarm platform is described next. Following chapters are dedicated to generation of material data, which are included in the database. These data are coefficients in material models, which include flow stress models, microstructure evolution models, phase transformation models and mechanical properties models. Several models of various complexity and various predictive capabilities were chosen for each mentioned phenomenon. All are mean field models to allow fast simulation of the whole manufacturing chain. Modern steel grades were selected as the case studies. Experimental tests performed to generate the data composed plastometric tests, stress relaxation tests and dilatometric tests. Inverse analysis was applied to determine the coefficients in the model. Discussion of results focused on validation and on new aspects of models recapitulates the paper.

The paper presents the design and implementation of a computer system dedicated to the optimization of a hot strip rolling process. The software system proposed here involves the flexible integration of virtual models of various devices used in the process: furnace, descalers, rolling stands, accelerated cooling systems, and coiler. The user can configure an arbitrary sequence of operations and perform simulations for this sequence. The main idea of the system and its implementation details are described in the paper. Besides the computer science part, the material models describing the rolling parameters, microstructure evolution, phase transformations, and product properties are also presented. Effect of precipitation was accounted for various stages of the rolling cycle. Experimental tests were performed to generate data for identification of the models. These include plastometric tests, two-step compression tests, and dilatometric tests. Following this, physical simulations of rolling cycles were performed on Gleeble 3800 to supply data for the verification and validation of the models. Finally, case studies of modern industrial processes were performed, and the selected results are presented.