Technological features of prognostication of properties of fusions and structure of metal of foundings for work in extreme terms
Performed a comprehensive study of the casting, mechanical and special properties of heat-resistant and wear-resistant iron-based alloys in a wide range of chromium concentration, manganese, aluminum, carbon, titanium, and so on, are designed latest heat-resistant and wear-resistant alloys, depending on the specific operating conditions of the cast parts in extreme conditions . It created a database covering more than 600 brands of iron-based alloys, ferro alloys and all existing brands of steel and iron scrap. Developed methodology and software quality prediction of the melt, which is in the melting unit, based on the results of the first chemical analysis of the structure and properties of metal castings and computer calculation of the charge for the smelting of high-chromium alloys using standard and non-standard charging materials. Novel molding and core mixtures for the production of high-quality castings of alloys and created a database to select the optimum recipe mixes.
According to the research of casting, mechanical and special properties of alloys with a high chromium content developed software to predict the quality of the melt, and a computer program for calculating the charge for the smelting of high alloy. This contributes to the improvement of the technological process of melting alloys provided the use of modern methods of rapid spectral analysis.
As the foundation of an optimization model pre-established number of regression models depending on the properties of the steel from its chemical composition for solving the direct problem of modeling - prediction: fluidity, linear shrinkage, the crack area, the total volumetric shrinkage, void volume, the volume of shells, tensile strength, impact strength and hardness . The presence of nine regression models makes it possible to create a complex (system) of the nine optimization (diagnostic) models for determining the chemical composition of the steel and to use each of these pre-determine which of the regression models will act as the optimization criterion.