Martin Zouhar

Pearlitic (stainless) steel is used in automotive, aerospace, and other industries where high strength, hardness, and wear resistance are required. Its quality control can be performed using mechanical tests or by examining the lamellar microstructure, namely, determining interlamellar distance. One of the related approaches is the circular line method (CLM). This paper reviews the challenges to automate employment of the CLM using custom Python code in order to reduce human time costs during image-based quality assessment of pearlite. The goal is to perform intersection counting automatically once the human operator has configured the application and selected the locations of measuring circles. Performance assessment using manually processed data from some 465 images is performed. We divide the imaged pearlite microstructures into different “types” when the code performs well or, respectively, not so well. We conclude with possible extensions of the work presented here.

The success of machine learning (ML) models in object or pattern recognition naturally leads to ML being employed in the classification of the microstructure of steel surfaces. Light optical microscopy (LOM) is the traditional imaging process in this field. However, the increasing use of ML to extract or relate more aspects of the aforementioned materials and the limitations of LOM motivated us to provide an improvement to the established image acquisition process. In essence, we perform style transfer from LOM to scanning electron microscopy (SEM) combined with “intelligent” upscaling. This is achieved by employing an ML model trained on a multimodal dataset to generate an SEM-like image from the corresponding LOM image. This transformation, in our opinion, which is corroborated by a detailed analysis of the source, target and prediction, successfully pushes the limits of LOM in the case of steel surfaces. The expected consequence is the improvement of the precise characterization of advanced multiphase steels’ structure based on these transformed LOM images.

This paper aims to elucidate the effect of an air‐formed native oxide covering mild steel surface on the contrast in the scanning electron microscopy (SEM) images obtained with the landing energy from 5 keV down to 0 eV. Part of the mild steel surface was in‐situ cleaned by Ar ⁺ ion sputtering process in order to remove native oxide from the surface. It enabled us to observe the oxide‐free and the naturally oxidized area on the mild steel surface simultaneously in the SEM micrographs. Presence of the native oxide starts to play a role in the SEM images acquired at landing energy below roughly 3 keV. Contrast between differently oriented grains situated inside the area covered by the native oxide starts to be negligible with landing energy decreasing below 3 keV, up to some ultra‐low values where the contrast increases again. Total reflectivity contrast between the clean and the oxidized area increases exponentially with landing energy decreasing below 3 keV. The reflectivity‐versus‐energy curves of the cleaned and the naturally oxidized mild steel surface are markedly different. The reflectivity of the electrons is correlated with the density of states (DOS), as is demonstrated at very low landing energies. Sensitivity of the very low‐energy electrons to the electronic structure was verified by comparison of the experimental data with the simulations of reflectivities, band structure, and DOS. The theoretical predictions are based on the density‐functional theory calculations and they have been performed in energy range corresponding to specular reflectivities of the Fe‐BCC (001) orientation. We have also observed that close to the mirror condition, that is, near‐zero landing energies, the primary electrons become sensitive to the surface potential differences caused by the work function differences of clean and native oxide‐covered steel surfaces.

The segmented semiconductor detectors for transmitted electrons in ultrahigh resolution scanning electron microscopes allow observing samples in various imaging modes. Typically, two standard modes of objective lens, with and without a magnetic field, differ by their resolution. If the beam deceleration mode is selected, then an electrostatic field around the sample is added. The trajectories of transmitted electrons are influenced by the fields below the sample. The goal of this paper is a quantification of measured images and theoretical study of the capability of the detector to collect signal electrons by its individual segments. Comparison of measured and ray-traced simulated data were difficult in the past. This motivated us to present a new method that enables better comparison of the two datasets at the cost of additional measurements, so-called calibration curves. Furthermore, we also analyze the measurements acquired using 2D pixel array detector (PAD) that provide a more detailed angular profile. We demonstrate that the radial profiles of STEM and/or 2D-PAD data are sensitive to material composition. Moreover, scattering processes are affected by thickness of the sample as well. Hence, comparing the two experimental and simulation data can help to estimate composition or the thickness of the sample.

The detailed examination of electron scattering in solids is of crucial importance for the theory of solid-state physics, as well as for the development and diagnostics of novel materials, particularly those for micro- and nanoelectronics. Among others, an important parameter of electron scattering is the inelastic mean free path (IMFP) of electrons both in bulk materials and in thin films, including 2D crystals. The amount of IMFP data available is still not sufficient, especially for very slow electrons and for 2D crystals. This situation motivated the present study, which summarizes pilot experiments for graphene on a new device intended to acquire electron energy-loss spectra (EELS) for low landing energies. Thanks to its unique properties, such as electrical conductivity and transparency, graphene is an ideal candidate for study at very low energies in the transmission mode of an electron microscope. The EELS are acquired by means of the very low-energy electron microspectroscopy of 2D crystals, using a dedicated ultra-high vacuum scanning low-energy electron microscope equipped with a time-of-flight (ToF) velocity analyzer. In order to verify our pilot results, we also simulate the EELS by means of density functional theory (DFT) and the many-body perturbation theory. Additional DFT calculations, providing both the total density of states and the band structure, illustrate the graphene loss features. We utilize the experimental EELS data to derive IMFP values using the so-called log-ratio method.