The excellent abrasion resistance of high-chromium cast irons (HCCIs) is given by an optimal combination of hard eutectic and secondary carbides (SC) and a supporting matrix. The tailoring of the microstructure is performed by heat treatments (HTs), with the aim to adjust the final properties (such as hardness and abrasion resistance). In this work, the influence of chemical composition on the microstructure and hardness of HCCI_26%Cr is evaluated. An increase in the matrix hardness was detected after HTs resulting from combining precipitation of M₂₃C₆ SC during destabilization, and austenite/martensite transformation during quenching. Kinetic calculations of the destabilization process showed that M₇C₃ secondary carbides are the first to precipitate during heating, reaching a maximum at 850 °C. During subsequent heating up to 980 °C and holding at this temperature, they transformed completely to M₂₃C₆. According to the MatCalc simulations, further precipitation of M₂₃C₆ occurred during cooling, in the temperature range 980–750 °C. Both phenomena were related to experimental observations in samples quenched after 0-, 30-, 60- and 90-min destabilization, where M₂₃C₆ SC were detected together with very fine SC precipitated in areas close to eutectic carbides.

The excellent abrasion resistance of high chromium cast irons (HCCIs) stems from the dispersion of the hard iron-chromium eutectic carbides. The surrounding matrix on the other hand, provides sufficient mechanical support, improving the resistance to cracking deformation and spalling. Prior knowledge of the microstructural characteristics is imperative to appropriately design subsequent heat treatments, and in this regard, employing computational tools is the current trend. In this work, computational and experimental results were correlated with the aim of validating the usage of MatCalc simulations to predict the eutectic carbide phase fraction and the elemental distribution in two HCCI alloys, in the as-cast condition. Microstructural observations were carried out using optical microscopy and SEM. The chemical composition and fraction of each phase was measured by electron probe microanalysis and image analysis, respectively. In all cases, the values predicted by the pseudo-equilibrium diagrams, computed with MatCalc, were in accordance with the experimentally determined values. Consequently, the results suggest that time and resource intensive experimental procedures can be replaced by simulation techniques to determine the phase fraction and especially, the individual phase compositions in the as-cast state.

In the current study, an as-cast 26% Cr high chromium cast iron (HCCI) alloy was subjected to dry-sliding linear wear tests, under different loads. The loads were selected based on analytically computing the critical load (P_C) i.e., the load necessary to induce plastic deformation. The P_C was calculated to be 15 N and accordingly, a sub-critical load (5 N) and an over-critical load (20 N) were chosen. The influence of increasing the load during the wear test was investigated in terms of the matrix microstructural behaviour and its ability to support the surrounding carbides. The morphological aspects of the wear tracks, and the deformed matrix microstructure adjacent and underneath the track was analysed by confocal laser scanning microscope (CLSM) and scanning electron microscope (SEM), respectively. No evidence of plastic deformation of the matrix was observed below P_C. On the contrary, at loads equal to and higher than P_C, the austenitic matrix plastically deformed as evidenced by the presence of slip bands. Electron backscattered diffraction (EBSD) measurements in terms of grain reference orientation deviation, and micro-Vickers hardness of the austenitic matrix indicated a deformation depth of about 40 µm at the maximum applied load of 20 N. The active wear mechanisms during sliding were a combination of both adhesive and abrasive wear, although increasing the load shifted the dominant mechanism towards abrasion. This was primarily attributable to the increased propensity for carbide cracking and fracturing, combined with the inability of the hardened austenitic matrix surface and sub-surface to adequately support the broken carbide fragments. Moreover, the shift in the dominant wear mechanism was also reflected in the wear volume and subsequently, the wear rate.

By employing destabilization heat treatments (HT), it is possible to create microstructures possessing different fractions of carbides, martensite, and austenite, which lead to varying tribological responses in abrasion-resistant high-chromium white cast irons. In the current work, the destabilization temperature was kept constant at 980 °C, whereas the time was varied from 0 to 90 min. As a result, the microstructure of the 26 wt% Cr white cast iron had a mixture of M₂₃C₆ secondary carbides (SC), martensite, and a decrease in the amount of retained austenite (RA) with increasing destabilization holding time. The microstructures as well as their tribological characteristics were evaluated by combining confocal laser scanning microscopy, SEM, XRD, and EBSD, together with dry-sliding linear reciprocating wear tests. Results show that the volume fraction of SC were statistically comparable in samples destabilized for 0 and 90 min, although the average size was almost two-fold in the latter. This had direct implications on the wear properties where a decrease of up to 50% in the wear rate of destabilized samples compared to the non-treated material was observed. Furthermore, the sample with the lowest increase in the matrix hardness (~ 20% higher than non-treated), showed the highest wear resistance. This was attributed to a favourable distribution of the RA (~ 10%) and SC volume fraction (~ 5%), in combination with the harder martensitic matrix. Finally, the results obtained from this study shed light on the ability to alter the HT parameters to tune the microstructure depending upon the application prerequisite. Graphical

In view of the paradigm shift toward data-driven research in materials science and engineering, handling large amounts of data becomes increasingly important. The application of FAIR (findable, accessible, interoperable, reusable) data principles emphasizes the importance of metadata describing datasets. We propose a novel data processing and machine learning (ML) pipeline to extract metadata from micrograph image files, then combine image data and their metadata for microstructure classification with a deep learning approach compared to a classic ML approach. The ML model attained excellent performances with and without metadata and bears potential for performance improvement of further use cases within the community. Graphical abstract