The prior austenite grain size (PAGS) represents one of the most significant microstructural parameters for steel research and process development. Since the PAGS directly correlates with recrystallisation during rolling in the manufacturing process of steel plates, it has a huge influence on its mechanical properties. Methods to determine the PAGS reliably and reproducibly are in high demand. There are several different approaches, based on different working principles, aiming to measure the PAGS. In this paper, the focus will be held on chemical etching methods because they allow, other than indirect techniques, space-resolved images as output, coupled with a fast application with good statistics and do not necessarily require a pretreatment of the specimen that can alter properties of interest. A parameter study has been conducted to identify unknown influencing variables as well as to tune well known parameters for their application to low-carbon steels. In the scope of this work, a novel and objective way of determining the PAGS is being presented. A reproducible approach has been developed that is able to automatically reconstruct the prior austenite grain boundaries (PAGB) from low-carbon steels and thereby determining the PAGS. Based on an improved etching recipe, a routine could be elaborated using modern methods of machine learning in the field of computer vision that is able to quantitatively analyze optical micrographs. Semantic segmentation is used to detect the PAGB based on correlative EBSD data and expert’s annotations; thus, reconstructing the prior morphological microstructure. Therefore, besides the determination of the average grain size, the distribution of the PAGS and their morphological parameters can be quantified.

Modern dual‐phase (DP) steels are widely used in industry due to their favorable material behavior based on the complex microstructure. In the presented work, a microstructure based FEM model for the elasto‐plastic deformation of DP steels is developed in order to accomplish a deeper understanding of the structure‐property correlation. The underlying microstructure is obtained by serial section tomography. The following contribution deals with a comparison of different approaches to build a FE mesh, which is based on such microstructures. (© 2016 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Dual-phase steel shows a strong connection between its microstructure and its mechanical properties. This structure–property correlation is caused by the composition of the microstructure of a soft ferritic matrix with embedded hard martensite areas, leading to a simultaneous increase in strength and ductility. As a result, dual-phase steels are widely used especially for strength-relevant and energy-absorbing sheet metal structures. However, their use as heavy plate steel is also desirable. Therefore, a better understanding of the structure–property correlation is of great interest. Microstructure-based simulation is essential for a realistic simulation of the mechanical properties of dual-phase steel. This paper describes the entire process route of such a simulation, from the extraction of the microstructure by 3D tomography and the determination of the properties of the individual phases by nanoindentation, to the implementation of a simulation model and its validation by experiments. In addition to simulations based on real microstructures, simulations based on virtual microstructures are also of great importance. Thus, a model for the generation of virtual microstructures is presented, allowing for the same statistical properties as real microstructures. With the help of these structures and the aforementioned simulation model, it is then possible to predict the mechanical properties of a dual-phase steel, whose three-dimensional (3D) microstructure is not yet known with high accuracy. This will enable future investigations of new dual-phase steel microstructures within a virtual laboratory even before their production.

No abstract

No abstract