No abstract

Fast progress in modeling of metal processing encourage researchers to look for better technologies, which can be done through optimization of their design. Authors have developed the computer system ManuOpti for optimization of manufacturing chains based on materials processing. Application of this system to simulations and optimization of manufacturing of automotive parts was the general objective of the paper. ManuOpti software enables performing optimization by the user with little experience in the computer science and in the optimization methods. On the other hand, the application of the optimization techniques is efficient only when reliable material models and accurate numerical methods are applied. Therefore, validation of models describing microstructure evolution in automotive steel (Dual Phase-DP) was the next objective of the paper. Physical simulations of thermal cycles were performed and the experimental results were used to validate the model. Numerical tests with the ManuOpti system recapitulate the paper. Case studies for the tests included various thermal cycles of the continuous annealing of DP steels.

The model describing evolution of dislocation population based on fundamental works of Kocks, Estrin and Mecking (KEM) is a useful tool in modelling of metallic materials processing. In combination with the Sandstrom and Lagneborg approach it can predict changes of the dislocation density accounting for hardening, recovery and recrystallization. Numerical solutions of a one-parameter model (average dislocation density), as well as for two types of dislocations and three types of dislocation are described in the literature. All these solutions were performed for deterministic variables. On the other hand, an advanced modelling of materials requires often an information about distribution of parameters. This is the case when uncertainty of the model has to be evaluated or when an information about distribution of product properties is needed. The latter is crucial when deterioration of local formability is caused by sharp gradients of properties. Thus, the investigation of possibilities of numerical solution for the KEM model with stochastic variables was the main objective of the present work. Evolution equation was written for the distribution function and solution was performed using Monte Carlo method. Analysis of the results with respect to the reliability and computing costs was performed. The conclusions towards selection of the best approach were formulated.