Open AccessPREDICTION OF FATIGUE CRACK GROWTH PROCESS VIA ARTIFICIAL NEURAL NETWORK TECHNIQUE
Author(s) -
Konstantin N. Nechval,
Nicholas A. Nechval,
Irina Bausova,
Daina Šķiltere,
Vladimir F. Strelchonok
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.5.3.406
Subject(s) - artificial neural network , paris' law , aerospace , process (computing) , computer science , relation (database) , measure (data warehouse) , structural engineering , fatigue testing , artificial intelligence , fracture mechanics , engineering , data mining , crack closure , aerospace engineering , operating system
Failure analysis and prevention are important to all of the engineering disciplines, especially for the aerospace industry. Aircraft accidents are remembered by the public because of the unusually high loss of life and broad extent of damage. In this paper, 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.