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dc.contributor.authorNechval, Konstantin N.
dc.contributor.authorBausova, Irina
dc.contributor.authorŠķiItere, Daina
dc.contributor.authorNechval, Nicholas A.
dc.contributor.authorStrelchonok, Vladimir F.
dc.coverage.spatialBrestru_RU
dc.date.accessioned2023-11-27T08:27:24Z
dc.date.available2023-11-27T08:27:24Z
dc.date.issued2006
dc.identifier.citationFatigue 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.urihttps://rep.bstu.by/handle/data/37209
dc.description.abstractThe 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.isoenru_RU
dc.publisherBrSTUru_RU
dc.subjectartificial neural networkru_RU
dc.subjectискусственная нейронная сетьru_RU
dc.titleFatigue Crack Growth Prediction via Artificial Neural Network Techniqueru_RU
dc.title.alternativeПрогнозирование роста усталостных трещин с помощью метода искусственной нейронной сетиru_RU
dc.title.alternativeПрогнозирование роста усталостных трещин Метод искусственных нейронных сетейru_RU
dc.typeНаучный доклад (Working Paper)ru_RU


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