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 demonstrate a novel technique enabling the accurate visualization and fast mapping of deformation-induced α′-martensite produced during cyclic straining of a metastable austenitic stainless steel, refined by reversion annealing to different grain sizes. The technique is based on energy and angular separation of the signal electrons in a scanning electron microscope (SEM). Collection of the inelastic backscattered electrons emitted under high take-off angles from a sample surface results in the acquisition of micrographs with high sensitivity to structural defects, such as dislocations, grain boundaries, and other imperfections. The areas with a high density of lattice imperfections reduce the penetration depth of the primary electrons, and simultaneously affect the signal electrons leaving the specimen. This results in an increase in the inelastic backscattered electrons yielded from the vicinity of α′-martensite, and a bright halo surrounds this phase. The α′-martensite phase can thus be separated from the austenitic matrix in SEM micrographs. In this work, we propose a deep learning method for a precise α′-martensite mapping within a large area. Various deep learning-based methods have been tested, and the best result measured by both Dice loss and IoU scores has been achieved using the U-Net architecture extended by the ResNet encoder.

Even small changes in microstructure can cause great changes of mechanical properties. A great variation in microstructure – especially in the case of steel – leads to a wide range of mechanical properties [ 1]. Hence it is important to study microstructure. Unfortunately, a universal study method does not exist, thus we need to rely on a variety of different microstructure imaging techniques. However, navigating the sample and finding the corresponding region in scale of a few micrometers in different devices (and imaging techniques) is difficult, especially if devices are from different manufacturers. The existing multi-modal solutions typically assume either integrated multiple detectors in a chamber or a single sample carrier that can be switched among different machines of the same provider. But we needed to develop a customized technique to obtain a large number of images of corresponding regions with our equipment. Specifically, it is FEI Magellan 400L scanning electron microscope (SEM, using CBS detector) [ 2], the Keyence VK-X1000 confocal laser scanning microscope (CLSM) [ 3] and the Zeiss Axio Observer 7 for materials light optical microscope (LOM) [ 4].

The preparation of metallographic samples remains a crucial aspect of microstructural analysis, especially with the continuous development of advanced materials and imaging techniques. Despite its significance, sample preparation is often underestimated, yet achieving a surface with minimal structural distortion is essential for accurate microstructure evaluation and data interpretation. This study aimed to optimize steel sample preparation methods to obtain surfaces suitable for correlative imaging using multiple microscopic techniques, including modern scanning electron microscopy (SEM) with sample bias and electron backscatter diffraction (EBSD). The results demonstrate that specific contrast features observed in SEM can, in some cases, be qualitatively verified using EBSD. Furthermore, variations in SEM settings, such as lower landing energy, influence information depth, which in turn affects the accuracy of phase quantification, particularly when utilizing artificial intelligence-based methods.