| dc.contributor.author | Nechval, Konstantin N. | |
| dc.contributor.author | Bausova, Irina | |
| dc.contributor.author | ŠķiItere, Daina | |
| dc.contributor.author | Nechval, Nicholas A. | |
| dc.contributor.author | Strelchonok, Vladimir F. | |
| dc.coverage.spatial | Brest | ru_RU |
| dc.date.accessioned | 2023-11-27T08:27:24Z | |
| dc.date.available | 2023-11-27T08:27:24Z | |
| dc.date.issued | 2006 | |
| dc.identifier.citation | Fatigue Crack Growth Prediction via Artificial Neural Network Technique / Konstantin N. Nechval [et al,] // International Conference on Neural Networks and Artificial Intelligence : proceedings, Brest, 31 May – 2 June, 2006 / Edited: V. Golovko [et al.]. – Brest : BSTU, 2006. – P. 124–129 : il. – Bibliogr.: p. 128–129 (26 titles). | ru_RU |
| dc.identifier.uri | https://rep.bstu.by/handle/data/37209 | |
| dc.description.abstract | The artificial neural network (ANN) technique for the data processing of on-line fatigue crack growth monitoring is proposed after analyzing the general technique for fatigue crack growth data. A model for predicting the fatigue crack growth by ANN is presented, which does not need all kinds of materials and environment parameters, and only needs to measure the relation between a (length of crack) and N (cyclic times of loading) in-service. The feasibility of this model was verified by some examples. It makes up the inadequacy of data processing for current technique and on-line monitoring. Hence it has definite realistic meaning for engineering application. | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | BrSTU | ru_RU |
| dc.subject | artificial neural network | ru_RU |
| dc.subject | искусственная нейронная сеть | ru_RU |
| dc.title | Fatigue Crack Growth Prediction via Artificial Neural Network Technique | ru_RU |
| dc.title.alternative | Прогнозирование роста усталостных трещин с помощью метода искусственной нейронной сети | ru_RU |
| dc.title.alternative | Прогнозирование роста усталостных трещин Метод искусственных нейронных сетей | ru_RU |
| dc.type | Научный доклад (Working Paper) | ru_RU |
