In the article research results are presented, which aim to provide evaluation of thermal protection properties of volume textile materials. However, as a result of experts wearing it has been revealed that by their operational performance their characteristicsare quite high to such materials: Holofiber, Tinsulate, Arctic, etc. At the present time to research thermal protection properties of sewing materials methods are used that can be divided into 2 groups: Methods based on the principle of steady heat mode and Methods based on the principle of unsteady (regular) mode. New device has been developed which allows to simplify both the schematic diagram and the methodological approach to experimental evaluation of thermal protection properties of volume textile materials. The corresponding experimental research were held based on the developed bicalorimeter. Study results allowed to establish heat insulation material «ArcticP» possesses the highest thermal resistance.It is located with its metallized coating facing outside. High values of thermal protection properties of this material are explained by availability of metallized coating from outer side which ensures partial heat reflection.. The research was made in Don State Technical University within the framework of State Assignment of the Ministry of education and science of Russia under the project 11.9194.2017/БЧ.
The article is devoted to the development of machine learning methods for classes of technical problems, including determining the properties of materials. According to the authors, the neural network approximation algorithm is able to take into account the behavior of materials in various experimental conditions. The article provides illustrative examples of how a neural network with a single hidden layer can approximate a function of several variables with a given accuracy. As part of the study, a number of experimental measurements were made. The structure of the neural network and its main components are described.
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