The current paper studies the kinetics features of supercooled austenite decomposition in the pearlite temperature range (600-500 C) in chromiummanganese cast iron with the content of 2.2% carbon, 12.63% chromium, 5.7% manganese. The structure formation, phase composition, hardness as well as the distribution of elements between the phases and the structural constituents of the above-mentioned cast iron after isothermal soaking are investigated. The average size of secondary carbides after isothermal soaking is determined using JMicroVision v.1.2.7 free software.
The object of research in this work was cast iron for machine-building parts, alloyed with Al. The possibility of improving the mechanical properties of cast iron by choosing the optimal Mn – Al combinations, depending on the carbon content in the cast iron, was determined. The study was carried out on the basis of available retrospective data of serial industrial melts by constructing the regression equation for the ultimate strength of cast iron in the three-factor space of the input variables C – Mn – Al. The optimization problem was solved by the ridge analysis method after reducing the dimension of the factor space by fixing the carbon content at three levels: C = 3 %, C = 3.3 %, and C = 3.6 %.
It was found that the maximum values of the ultimate strength are achieved at the minimum level of carbon content (C = 3%) and are in the range of values close to 300 MPa. In this case, the Al content is in the range (2.4–2.6) %, and the Mn content is about 0.82 %. With an increase in the carbon content, there is a tendency to a decrease in the content of Mn and Al in the alloy, which is necessary to ensure the ultimate strength close to 300 MPa. The results of the ridge analysis of the response surface also showed that at the upper limit of the carbon content (C = 3.6%), it is not possible to reach the ultimate strength of 300 MPa in the existing range of Mn and Al variation.
All solutions are verified for the following ranges of input variables C = (2.94–3.66) %, Mn = (0.5–1.1) %, Al = (1.7–2.9) %.
Graphical-analytical descriptions of the optimal Mn – Al ratios are obtained, depending on the actual content of carbon in the alloy, which make it possible to purposefully select the optimal melting modes by controlling the tensile strength of the alloy
The peculiarities of modern military conflicts significantly increase the requirements for the efficiency of object state assessment. Therefore, it is necessary to develop algorithms (methods and techniques) that can assess the state of the monitoring object from different sources of intelligence for a limited time and with a high degree of reliability. Accurate and objective object analysis requires multi-parameter estimation with significant computational costs. That is why the following tasks were solved in the study: the formalization of the assessment of monitoring objects was carried out, a method of increasing the efficiency of assessing the condition of monitoring objects was developed and an efficiency assessment was carried out. The essence of the proposed method is the hierarchical hybridization of binary classifiers and their subsequent training.
The method has the following sequence of actions: determining the degree of uncertainty, constructing a classifier tree, determining belonging to a particular class, determining object parameters, pre-processing data about the object of analysis and hierarchical traversal of the tree.
The novelty of the method lies in taking into account the type of uncertainty and noise of the data and taking into account the available computing resources of the object state analysis system. The novelty of the method also lies in the use of combined training procedures (lazy training and training procedure for evolving neural networks) and selective use of system resources by connecting only the necessary types of detectors.
The method allows you to build a top-level classifier using various low-level schemes for combining them and aggregating compositions. The method increases the efficiency of data processing by 12–20 % using additional advanced procedures
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