Physical and Numerical Simulations for Predicting Distribution of Microstructural Features during Thermomechanical Processing of Steels.

Łukasz Poloczek,Jan Kusiak,Valeriy Pidvysots’Kyy,Roman Kuziak,Danuta Szeliga,Maciej Pietrzyk

doi:10.3390/ma15051660

Łukasz Poloczek, Jan Kusiak + Show 4 more

Open Access

https://doi.org/10.3390/ma15051660

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Abstract

The design of modern construction materials with heterogeneous microstructures requires a numerical model that can predict the distribution of microstructural features instead of average values. The accuracy and reliability of such models depend on the proper identification of the coefficients for a particular material. This work was motivated by the need for advanced experimental data to identify stochastic material models. Extensive experiments were performed to supply data to identify a model of austenite microstructure evolution in steels during hot deformation and during the interpass times between deformations. Two sets of tests were performed. The first set involved hot compressions with a nominal strain of 1. The second set involved hot compressions with lower nominal strains, followed by holding at the deformation temperature for different times. Histograms of austenite grain size after each test were measured and used in the identification procedure. The stochastic model, which was developed elsewhere, was identified. Inverse analysis with the objective function based on the distance between the measured and calculated histograms was applied. Validation of the model was performed for the experiments, which were not used in the identification. The distance between the measured and calculated histograms was determined for each test using the Bhattacharyya metric and very low values were obtained. As a case study, the model with the optimal coefficients was applied to the simulation of the selected industrial hot-forming process.

Highlights

Enhancing the strength–ductility synergy of materials has been an objective of research on structural materials for years
The successful control or application of chemical or microstructural heterogeneity to achieve the desired properties was achieved by some researchers, it seems that numerical modelling can still be useful support for the design of these materials
In Ref. [21], the grain size was included as the second stochastic variable, and the identification of the model based on the experimental data available in the literature for medium-carbon steel was performed

Summary

Introduction

Enhancing the strength–ductility synergy of materials has been an objective of research on structural materials for years. The analysis of the published works shows that there is a need for the development of the inverse approach to the identification of the stochastic microstructure models of steels This approach requires advanced experiments, which supply information about the heterogeneity of microstructural parameters instead of the average values. [21], the grain size was included as the second stochastic variable, and the identification of the model based on the experimental data available in the literature for medium-carbon steel was performed. This identification, requires a special set of experimental data, including histograms of microstructure parameters at various stages of the process

Experiment

Model Validation

Conclusions