Universal model for predicting the phase composition of multicomponent brasses based on chemical analysis data

When developing technical requirements for alloys, it is important to apply an integrated approach. Combining analytical and simulation modelling, it is possible to reduce technological risks at the stage of creating or modifying requirements. The implementation of this approach directly depends on the degree of consideration of all factors included in the models, as well as on their influence on the variability of characteristics. However, known models do not provide satisfactory convergence with real industrial alloys. Using the example of a complex-alloyed CuZn13Mn8Al5Si2Fe1Pb brass, an approach is proposed that allows describing the variability in the structural state of multicomponent brasses. The analysis of statistical data on the chemical composition and microstructure of industrial batches, made it possible to establish that the alloy matrix solution is a (α+β)-brass, and corresponds to the phase ratio at 700 °C on the polythermal pseudo-binary cross-section of the Cu–Zn–Mn₅Si₃ diagram. The distribution of alloying elements in the main phases was studied using X-ray spectral analysis. The complete binding of iron in silicides and uniform distribution of manganese in the hot-pressed state were confirmed. A calculation of the silicon proportion in the solid solution was proposed. The measured density of the alloy is 7650 kg/m³, while the calculated density of the matrix solution is 8100 kg/m³. Based on the updated parameters of the universal model, the authors used the Monte Carlo method to assess the variability of the microstructure in relation to the requirements for the chemical composition. The instability of technological properties is attributed to significant variability in the ratio of the α- and β-phases. The content of the α-phase in the alloy ranges from 37.5 % to 66.5 %, while the β-phase varies from 17.5 % to 55.2 %. The simulation model developed in this study enables both to analyse the existing alloys and to predict the behaviour of new alloys. This is critically important for optimising technological processes, and improving the operational properties of materials.