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The need for a reliable prediction of the distribution of microstructural parameters in metallic materials during processing was the motivation for this work. The model describing the evolution of dislocation populations, which considers the stochastic aspects of occurring phenomena, was formulated. The validation of the presented model requires the application of proper parameters corresponding to the considered materials. These parameters have to be identified through the inverse analysis, which, on the other hand, uses optimization methods and requires the formulation of the appropriate objective function. In our case, where the model involves the stochastic parameters, it is a crucial task. Therefore, a specific form of the objective function for the inverse analysis was developed using a measure based on histograms. The elaborated original stochastic approach to modeling the phenomena occurring during the thermomechanical treatment of metals was validated on commercially pure copper and selected multiphase steel.

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Product properties for innovative materials, e.g. dual phase steels, require precise control of production processes. Difficulties in optimization of process parameters correspond with large number of control variables, which should be considered in the technology design. Sensitivity analysis allows evaluating the importance of all process inputs on the final properties of material. Information on the most important inputs is crucial for further design of the process. Application of sensitivity analysis requires detailed knowledge of the process phenomena as well as the definition of the mathematical model of the thermomechanical process. Furthermore, some sensitivity analysis algorithms are of the high computational cost. Presented work concerns possibility of the application of data exploration approach in evaluation of the importance of process inputs as the alternative for sensitivity analysis. Use of data mining algorithms eliminates necessity of mathematical model development, it also does not require any apriori knowledge about the process. Authors presents the comparison of sensitivity analysis and data exploration approach in evaluating relationships between inputs and outputs of the hot rolling for dual phase steel strips. The presented approach and the perspectives of the practical application could lead to significant decrease of time necessary for the computations of process design. The theoretical considerations are supplemented with the results of both types of analysis.

Manufacturing of dual phase (DP) steels products is a complex process. Application of special thermal cycles during continuous annealing is one of the methods of obtaining DP microstructures. Development and validation of the numerical model for the continuous annealing of DP steels was the objective of the paper. Experimental part include dilatometric tests and physical simulations of various thermal cycles characteristic for the continuous annealing. The former were combined with the inverse analysis and were used for identification of the model. The latter supplied data regarding microstructure and properties after various annealing cycles. Numerical tests of the model confirmed its good predictive capabilities.

Physical and numerical simulations of the hot rolling and laminar cooling of DP steel strips are presented in the paper. The objectives of the paper were twofold. Physical simulations of hot plastic deformation were used to identify and validate numerical models. Validated models were applied to simulate the manufacturing of DP steel strips. Conventional flow stress model and microstructure evolution model were used in the hot deformation part. The approach to the complex systems analysis based on global thermodynamic characterization and detailed microstructure characterization was applied to determine equilibrium state at various temperatures. Finally, two numerical models were used to simulate kinetics of austenite decomposition at varying temperatures: the first, conventional model based on the Avrami equation, and the second, the discrete Cellular Automata approach. Plastometric tests and stress relaxation tests were used for identification of the hot rolling model for the DP steel. Dilatometric tests were performed to identify the phase transformation models. Verification confirmed good accuracy of all models. Validated models were applied to simulate the manufacturing of DP steel strips. Influence of technological parameters (e.g., strip thickness and velocity, active sections in the laminar cooling, and water flux in the sections) on the DP microstructure was analyzed. The cooling schedules, which give required microstructures were proposed. The numerical tool, which simulates manufacturing chain for DP steel strips is the main output of the paper.