## 1 Introduction
It can be observed that the spheroidization process carried out at elevated temperature resulted in coagulation and spheroidization of the particles, increasing the proportion of larger size carbides, effectively improving the wear resistance of the material. A similar phenomenon was observed in the case of machining. Heat treatment involving quenching and multiple tempering of the material also confirmed that, as the uniformity of the carbide size distribution increases, an increase in the wear resistance of the high-carbon material can be expected [19]. Studies on D2 steel showed that both primary and secondary carbides have a significant effect on wear resistance, with smaller primary carbides reducing crack initiation [20]. This effect may be related to the deformation of the carbide itself during the intense tribological interaction, which leads to an increase in hardness while contributing to brittleness and accelerated wear. Therefore, significant differences between the cementie separations can affect the behavior of the material under severe deformation in different ways [21]. In bearing steels, the densely distributed spherical carbides were effective in inhibiting crack propagation, resulting in an increase in toughness [22]. The impact of both tribological test conditions and the complexity of the morphology of carbide precipitates present in iron-based metallic materials indicates the necessity of using statistical tools to describe the phenomena occurring at the interface of interacting elements. The use of statistical models has found application in describing phenomena occurring in sintered materials, which, due to significant porosity, can exhibit considerable variability in results. Statistical models are used in describing the mechanical properties of sintered materials. Statistical ANOVA analysis has been used to optimize the sintering process and heat treatment of sintered Fe-Mn-Cr-Mo-C steels. Analysis of variance allowedfor the determination of relationships between sintering temperature, furnace atmosphere, processing time, and the hardness of the produced sintered components. Which of the analyzed variables significantly affected the obtained hardness results was identified. Similar relationships could also be established for the chemical composition of the tested steels. The variability in the chemical composition of the tested samples was correlated with the differences in hardness between the tested materials. This confirmed the significant impact of changes in chemical composition on the final properties of the tested samples [23; 24; 25]. In tribological studies, variance analysis was used to determine the influence of changes in lubricant parameters on the microstructure and surface quality of steel after mechanical processing. The results of statistical analysis indicate that, both ecologically and economically, the application of specific lubricant volumes and the selection of its chemical composition significantly affect tool wear and the surface condition of the material after processing [26]. Statistical ANOVA analysis was also used to evaluate the results of tribological tests for layers containing boron aimed at improving the wear resistance of railway crossings. The use of variance analysis allowed for the identification of the main factors affecting the material's wear resistance. It was confirmed that the obtained boron-rich layers significantly improved the material's wear resistance [27].