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Neural network models of the influence of the chemical composition of ore raw materials and parameters of melting processes on the properties of products

Studies of the influence of the chemical composition of ore raw materials on the properties of foundry products, as a rule, do not consider the features of production processes. The main reasons for this are that, firstly, the results of such research are usually limited to laboratory studies, secondly, the models do not take into account the specifics of the structure of ore raw materials, since these data can be obtained only after the completion of all thermophysical and chemical-energy-technological processes of heat treatment, thirdly, the use of models is difficult due to the need to promptly account for changes in the composition and values of systemic and external factors during heat treatment. The purpose of the paper is to design neural network models and tools that provide the possibility of adaptive structural and parametric adjustment to changes in the parameters of the analyzed processes of heat treatment of ore raw materials. Data on the chemical composition of ore raw materials and information on heat treatment processes are used as parameters of the projected neural network models. As a result of the research, the hypothesis about the possibility of indirect accounting for the influence of structural features of ore raw materials on the quality of products has been confirmed. Confirmation of this hypothesis will allow us to offer effective tools for operational management of thermophysical and chemical-energy-technological processes of heat treatment of ore raw materials. Read more...

Neural network forecasting of defects in the production of metallurgical products

The paper is devoted to the current problem of reducing the percentage of defective products produced at serial steelmaking plants. To study the patterns of defect formation, a neural network was created that predicts the formation of defects such as “crack” in castings produced by the open-hearth method. To train the neural network, statistical data on the chemical composition of ore raw materials and the corresponding values of the percentage of defects were used. The data was taken under the conditions of an ongoing serial production process, which led to a high degree of noise in the information both on the chemical composition of the ore raw material and on the mechanical properties based on the results of its heat treatment. Outliers of statistical information were detected and removed using the original author’s neural network technique. A neural network model for controlling thermophysical and chemical-energy-technological processes of thermal processing of ore raw materials was created on the basis of a perceptron-type neural network with sigmoid activation functions. By conducting virtual computer experiments on a neural network model, some important dependences of the probability of formation of the defects under study on the content of manganese, phosphorus, silicon, chromium and sulfur were identified. Based on the identified dependencies, practical recommendations have been developed to reduce the percentage of defects by adjusting the chemical composition of ore raw materials. Despite the relatively low accuracy of the developed neural network model, the application of the practical recommendations obtained made it possible to reduce the percentage of defective products manufactured in a large-scale production process by 2.51 times. Read more...