Production of heat-resistant EP220 and EP929 alloys by high-temperature treatment of melt

Analysis of samples of EP220 and EP929 alloys in the liquid and solid state permits the determination of the parameters for high-temperature melt treatment in their production. On heating to specific temperatures, the structure of the liquid alloys moves closer to equilibrium. In the solidification of such melt, the cast metal formed is characterized by finer grain structure, greater dispersity of the dendrites, and greater density and microhardness of the matrix. Industrial adoption of high-temperature melt treatment will improve plasticity, increase the long-term strength, and boost the product yield. The proposed technology does not fully utilize the potential of the alloy structure obtained after high-temperature melt treatment. The effect may be amplified by more prolonged holding of the melt at 1650°C and by optimization of the vacuum-arc heating, deformation, and heat treatment, in the light of the structural changes in the experimental samples of solid metal. © 2013 Allerton Press, Inc

Modeling of surface dust concentration in snow cover at industrial area using neural networks and kriging

Modeling of spatial distribution of pollutants in the urbanized territories is difficult, especially if there are multiple emission sources. When monitoring such territories, it is often impossible to arrange the necessary detailed sampling. Because of this, the usual methods of analysis and forecasting based on geostatistics are often less effective. Approaches based on artificial neural networks (ANNs) demonstrate the best results under these circumstances. This study compares two models based on ANNs, which are multilayer perceptron (MLP) and generalized regression neural networks (GRNNs) with the base geostatistical method-kriging. Models of the spatial dust distribution in the snow cover around the existing copper quarry and in the area of emissions of a nickel factory were created. To assess the effectiveness of the models three indices were used: the mean absolute error (MAE), the root-mean-square error (RMSE), and the relative root-mean-square error (RRMSE). Taking into account all indices the model of GRNN proved to be the most accurate which included coordinates of the sampling points and the distance to the likely emission source as input parameters for the modeling. Maps of spatial dust distribution in the snow cover were created in the study area. It has been shown that the models based on ANNs were more accurate than the kriging, particularly in the context of a limited data set. © 2017 Author(s)

Multilayer perceptron, generalized regression neural network, and hybrid model in predicting the spatial distribution of impurity in the topsoil of urbanized area

The study is based on the data obtained as a result of soil screening in the city of Noyabrsk, Russia. A comparison of two types of neural networks most commonly used in this type of research was carried out: multi-layer perceptron (MLP), generalized regression neural network (GRNN), and a combined MLP and ordinary kriging approach (MLPRK) for predicting the spatial distribution of the chemical element Chromium (Cr) in the surface layer of the urbanized territory. The model structures were developed using computer modeling, based on minimizing of a root mean squared error (RMSE). As input parameters, the spatial coordinates were used, and the concentration of Cr - as the output. The hybrid MLPRK approach showed the best prognostic accuracy. © 2018 Author(s)

The Influence of Melt State on Atomization Process and Quality of Powders on Iron and Nickel Base

The analysis of the results of physical and chemical properties and structure investigation in liquid multicomponent steels and alloys indicates that after melting their state is generally not in equilibrium. Heating to the critical temperatures helps the system to transform into the equilibrium state or the state close to it. Melt preparing before atomization affects the process of liquid metal dispersion and helps to improve the structure and properties of the powder. The optimum melt preparing technology before atomization leads to formation the dispersion dendritic structure, optimum morphology, quantity and size of primary and eutectic phases in the powders particles and increase in properties. © 2008 IOP Publishing Ltd

Modeling of changes in heat resistance of nickel-based alloys using bayesian artificial neural networks

Resource design of gas turbine engines and installations requires extensive information about the heat resistance of nickel-based superalloys, from which the most critical parts of aircraft and marine engines, pumps of gas-oil pumping stations and power plants are made. The problems are that the data on the heat resistance obtained as a result of testing each alloy under study are quite limited. In the present paper, the task of modelling changes in the heat resistance of nickel-based superalloy on the basis of available experimental data is solved. To solve the task, the most modern approach, the neural network modeling method, was applied. The input data are chemical compositions of heat-resistant nickel-based superalloys and the values of their heat resistance obtained experimentally. The output data are the calculated values of heat resistance modeled by an artificial neural network. In the course of the work, transformations of the input data were carried out to reduce the standard deviation of the modeling of the output data. The choice of the neural network configuration was made in order to achieve the highest possible accuracy. As a result, a neural network of direct error propagation was used, with 27 neurons on the input layer, 13 neurons in the hidden layer and 1 neuron in the output layer. To validate the results of the predictions, a group of alloys with the maximum number of known experimental values of heat resistance was randomly selected before the input of data into the network. After preparing the data, selecting the configuration and training the network, the chemical compositions of the selected group were loaded and their heat resistance values were calculated. Comparison of the obtained data with the experimental data showed high efficiency of the method. As a result, data on the change of heat resistance for the studied alloys were obtained and an analytical expression describing the obtained dependences was formulated. © 2020, Institute for Metals Superplasticity Problems of Russian Academy of Sciences. All rights reserved