Ondřej Ambrož

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.

In the field of materials analysis, chemical etching continues to play a crucial role in sample preparation, yet its approach is often more empirical. While classical metallographic methods dominate, insufficient understanding of the process creates room for innovation. To improve this situation, the development of new techniques allowing for structure observation during sample preparation is necessary. This is a critical step towards mitigating the influence of stochastic factors, thereby increasing the reliability and predictability of the outcomes. For real-time structural analysis during sample preparation, professional equipment was recently introduced. This contribution aims to verify in situ monitoring of structure during chemical etching using common laboratory equipment and a stereomicroscope with a camera and tablet. The method may encounter limitations due to the magnification and resolution capabilities of the stereomicroscope. Manipulation with a pipette and the risk of lens contamination with etchant can also be challenging. Additionally, etching in deeply dark etchants and ensuring adequate stirring of the etchant can be difficult. Nevertheless, this method offers new possibilities and can be beneficial for certain applications. To achieve a more comprehensive understanding, experiments with various materials and etchants are important to better grasp the limitations and possibilities of this innovative technique.

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.

Despite its predominantly empirical nature, metallographic etching remains an important sample preparation method that enables microstructural characterization. The poor repeatability is due to the ignorance of etching mechanisms and the human factor, which is mainly associated with the conventional approach. This approach is most commonly practiced by manual immersion of the sample in the etchant. The solution should be gently stirred so that reaction products do not settle on the surface and uneven etching does not occur. Often, stirring is provided by the sample itself or by a magnetic bar. In terms of spatial orientation, etching with the polished surface facing upwards allows the surface to be observed during the process. Some authors recommend etching with the polished surface downwards to minimize the deposition of reaction products. In some cases, a vertical orientation of the sample is recommended to reduce pitting. In this paper, the effect of the etchant stirring method on the surface quality of a metallographic structural steel specimen will be presented. In addition to different etchant stirring methods, different spatial orientations of the sample will also be investigated. To ensure better repeatability and accurate etching time, the samples will be etched using our automated apparatus. The resulting surface of the samples will be analysed using light optical microscopy (LOM) and confocal laser scanning microscopy (CLSM). The development of more reproducible etching methods is essential to obtain accurate microstructure information without artifacts and for advanced quantitative image analysis using deep learning.

Steel is an indispensable material in many industries because of its favourable properties and low cost compared to other materials. New advanced high strength steels (AHSS) are continuously being developed and the demands on material structure characterization are becoming greater [ 1]. The mechanical properties of steels in general are highly variable and are very sensitive to even a small change in structure. There are efforts to obtain as much information as possible about a specific sample location, which requires the simultaneous use of multiple techniques. The requirements for each technique are often conflicting. In a light microscope (LOM), a mechanically polished surface is only suitable for the study of macroscopic defects or inclusions that have a significantly different light reflectance. The reason for the impossibility of observing the real structure of the material under investigation is the formation of a plastically deformed layer. The thickness of this layer is determined by the pretreatment of the metallographic sample. From the process of material cutting, when thermal and mechanical damage to the material occurs, to the final mechanical polishing. The thickness of the deformed layer after cutting with a precise metallographic saw is in the order of units to tens of μm [ 2]. The primary requirement of each processing step is to remove the damage caused by the previous processing step [ 3]. In LOM, chemical etching is generally used to highlight the structure and remove most of the deformed layer after mechanical polishing. The main motivation of this study is to demonstrate the real structure of the material, which is achieved by etching grain boundaries, their surfaces or by selectively etching different phases. The conventionally prepared sample still has a residual layer, which is not disturbing and cannot be seen by the LOM. For the high sensitivity of modern low energy scanning electron microscopy (SEM), mechanical polishing as the last polishing step becomes insufficient. For SEM techniques, including an electron backscatter diffraction (EBSD), electropolishing as the last step of the whole preparation is preferably used. The surface is too smooth and the structure is not distinguishable in the LOM. For high quality EBSD data, chemical-mechanical polishing and vibratory polishing are also recommended as the last preparation step [ 4]. After this step, the structure is not clearly resolved in the SEM or in the LOM. The EBSD technique is surface sensitive due to shallow information depth, say a few nm in the case of iron. Chemical etching is generally not recommended because the induced surface roughening becomes even more apparent after tilting the sample [ 5]. The challenge is to visualize the same area using different microscopy techniques, including the real structure of advanced high-strength transformation induced plasticity (TRIP) steel and verify the phases using the EBSD.

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.

Chemical etching is an integral part of metallographic sample preparation. Maintaining precise etch times can be difficult and therefore repeatability is limited. The aim of this work is to improve the repeatability of sample preparation using robotization. Prior to etching, metallographic samples of S355J2 (1.0577) structural steel were finely mechanically polished. For verification, 15 specimens were prepared using an in-house designed automated etching machine with a built-in 5-axis robotic arm and 15 specimens prepared manually by an expert metallographer. The samples were etched with Kourbatoff no. 4 reagent for 8 seconds in a beaker placed in an ultrasonic cleaner at 80 kHz. The samples were then cleaned in 7 beakers of cleaning fluid also placed in the ultrasonic cleaner. The robotic etching and cleaning process was optimized and the quality of the resulting surface is at least as good as that of the samples prepared by an expert metallographer. The surfaces were compared using a light optical microscope (LOM) and a confocal laser scanning microscope (CLSM). The repeatability of the preparation process is a key aspect for obtaining a large dataset of steel microphotographs for training a deep neural network that will be used in future research.