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http://elartu.tntu.edu.ua/handle/lib/47610| Назва: | The use of neural networks for modeling the thermophysical characteristics of epoxy composites treated with electric spark water hammer |
| Автори: | Petpo, Stukhliak Vasyl, Martsenyuk Oleg, Totosko Danulo, Stukhlyak Iryna, Didych |
| Приналежність: | Ternopil Ivan Puluj National Technical University, Ruska 56, 46001 Ternopil, Ukraine University of Bielsko-Biala, Willowa St. 2, Bielsko-Biala, 43-300, Poland |
| Бібліографічний опис: | Stukhliak P., Martsenyuk V., Totosko O., Stukhlyak D., Didych I. (2024). The use of neural networks for modeling the thermophysical characteristics of epoxy composites treated with electric spark water hammer.CEUR Workshop Proceedings, 3742, 13–24. |
| Bibliographic citation (APA): | Stukhliak P., Martsenyuk V., Totosko O., Stukhlyak D., Didych I. (2024). The use of neural networks for modeling the thermophysical characteristics of epoxy composites treated with electric spark water hammer.CEUR Workshop Proceedings, 3742, 13–24. |
| Конференція/захід: | CEUR Workshop Proceedings |
| Дата публікації: | 12-чер-2024 |
| Дата внесення: | 23-січ-2025 |
| Видавництво: | CEUR-WS |
| Країна (код): | UA |
| Місце видання, проведення: | Ternopil, Ukraine, June 12–14, 2024 |
| Теми: | machine learning neural networks composite |
| Діапазон сторінок: | 13–24 |
| Серія/номер: | ;3742 |
| Короткий огляд (реферат): | In this work, the properties of epoxy composites modified with an active plasticizer were modeled. The material was treated with electrospark water hammer. The material was treated with electric spark water hammer, which improves their physical and mechanical properties. The main attention is paid to the study of the thermal coefficient of linear expansion, which is a critical parameter for the use of composites in different temperature conditions. The results of modeling the thermophysical characteristics showed a high correlation with the experimental data, where the correlation coefficient in the test sample was 0.99%. The prediction error of epoxy polymers filled with DEG-1, aluminum oxide, chromium oxide, and carbon black by neural networks is 0.11, 0.17, 0.93, and 0.04% in test samples for different fillers. It has been shown that neural networks are capable of analyzing data and learning from it. Therefore, modeling the properties of materials by neural networks allows achieving high prediction accuracy. |
| URI (Уніфікований ідентифікатор ресурсу): | http://elartu.tntu.edu.ua/handle/lib/47610 |
| Власник авторського права: | Stukhliak P., Martsenyuk V., Totosko O., Stukhlyak D., Didych I. |
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| Тип вмісту: | Article |
| Розташовується у зібраннях: | Навчальна література кафедри КТ |
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