Band 60, Heft 10

Two α-β brass alloys with varying Zn contents have been studied. The samples received for examination were heat-treated at 500 °C in order to conduct dezincification studies according to EN ISO 6509-1 and to measure the depth of dezincification from metallographic sections. According to the phase diagram, the α-phase content should increase as a result of heat-treatment at 500 °C. This phenomenon, however, is difficult to observe in the metallographic sections which were prepared from the heattreated samples. When analyzing the depth of dezincification, it was found that reduced dezincification occurred in the heat-treated samples. This is plausible since β brass is attacked by preferential corrosion. Not only was uniform layer dezincification observed in the brass alloy with the higher Zn content, but also the onset of plug dezincification. This is associated with the original microstructure of the brass which is characterized by a coarse primary grain size. In this alloy, it is clearly observed that larger areas containing alpha brass will impede the process of dezincification.

Electrolytic polishing is a finishing method that removes material from a metal or alloy through an anodic dissolution process. Etching can often be performed in the same electrolyte by simply reducing the applied voltage to 10 percent of that required for polishing. Manufacturers of metallographic equipment present their products as automated. Only the electrolysis process itself is automated. After finishing, the sample must be immediately removed manually by the operator and cleaned. This process is critical with regard to the quality of the final sample surface and safety, because hazardous substances are often handled. The robot is placed next to an electrolytic equipment and handles all sample movements and the cleaning process in the ultrasonic bath in this experiment. The samples are made from ER308LSi austenitic stainless steel using 3D printing by Wire Arc Additive Manufacturing (WAAM). The final surface is achieved electrolytically on the commercial equipment. The aim of the experiment is to compare the microstructure, especially with regard to the possibility of distinguishing delta ferrite. The surface is characterized using various microscopic techniques. Robotization can be the key to improving surface quality and safety.

Non-metallic inclusions (NMI) have a significant impact on the final properties of steel products. As of today, the scanning electron microscope equipped with energy-dispersive spectroscopy (SEM-EDS) serves as the state of art characterization tool to study NMIs in steel. The automated 2D analysis method with the SEM-EDS allows for a comprehensive analysis of all the inclusions observed within a selected area of the sample. The drawback of this method is the time taken to complete the analysis. Therefore, machine learning methods have been introduced which can potentially replace the usage of EDS for obtaining chemical information of the inclusion by making quick categorizations of the inclusion classes and types. The machine learning methods can be developed by either training it directly with labeled backscattered electron (BSE) images or by tabular data consisting of image features input such as morphology and mean gray value obtained from the BSE images. The current paper compares both these methods using two steel grades. The advantages and the disadvantages have been documented. The paper will also compare the usage of shallow and deep learning methods to classify the steels and discuss the outlook of the existing machine learning methods to efficiently categorize the NMIs in steel.

Traditional metallography relies on the imaging of individual section planes. However, conclusions as to spatial shapes and microstructural arrangements can only be drawn to a limited extent. The idea to reconstruct three-dimensional microstructures from metallographic serial sections is therefore obvious and not at all new. However, the manual process of preparing a great number of individual sections and assembling them into image stacks is time-consuming and laborious and therefore constitutes an obstacle to frequent use. This is why the Federal Institute for Materials Research and Testing, or BAM for short ( Bundesanstalt für Materialforschung und -prüfung ), is developing a robot-assisted 3D metallography system performing the tasks of preparation and image acquisition on a metallographic section fully automatically and repeatedly. Preparation includes grinding, polishing and optional etching of the section surface. Image acquisition is performed using a light optical microscope with autofocus at several magnification levels. The obtained image stack is then pre-processed, segmented and converted to a 3D model resembling a microtomographic image, but with a higher lateral resolution at large volumes. As opposed to tomographic techniques, it is possible to perform traditional chemical etching for contrasting. The integration of a scanning electron microscope is in the planning stages. Studies conducted so far have demonstrated the possibility of visualizing hot gas corrosion layers, gray cast irons and ceramic-based microelectronic structures (vias).