A novel vibration based non-destructive testing for predicting glass fibre/matrix volume fraction in composites using a neural network model

2016 ◽  
Vol 144 ◽  
pp. 96-107 ◽  
Author(s):  
N.I.E. Farhana ◽  
M.S. Abdul Majid ◽  
M.P. Paulraj ◽  
E. Ahmadhilmi ◽  
M.N. Fakhzan ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Glass Fibre ◽  
Volume Fraction ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Matrix Volume ◽  
Non Destructive ◽  
Fibre Matrix

Increasing the Safety of Operation of Industrial Technical Systems from the Composite Materials by Predicting Their Life on the Basis of New Methods of Non-Destructive Control and Deep Neural Networks

2021 ◽  
pp. 7-12
Author(s):  
S.O. Kozelskaya ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Composite Materials ◽  
Service Life ◽  
New Technologies ◽  
Preliminary Training ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
New Methods ◽  
Non Destructive

The problem is considered related to increase of the operational safety of industrial facilities made of composite materials by means of an a priori assessment of the maximum service life. Two tasks are being solved: development of the new methods and means of non-destructive testing allowing to identify the defects that appear in the process of testing products with various loads and in the process of their operation; development of the new methods and means for assessing service life of the products based on the results of non-destructive testing. The first problem is being solved by the development of optical-thermographic non-destructive testing, including the technologies of ultrasonic thermotomography and electric force thermography, which determine the state of the object by dynamic temperature fields and optical control technology based on the fiber-optic sensors that measure the amount of material internal deformation under a force effect on the structure. Solution to the second problem is based on the use of neural network analysis (artificial neural networks) for assessment and prediction of the service life using the results of non-destructive testing with preliminary training of the neural network. An estimate was obtained by the experimental studies related to the error in determining the products service life, which is 12.6 %. The implementation of the proposed approach will allow to create the new technologies for predicting the service life of elements and structures made of composite materials using the results of non-destructive testing, which will provide an additional opportunity for developing practical recommendations on the confirmation or extension of the service life and improvement of safety for structures operation.

scholarly journals Data traffic filtration of automated non-destructive testing based on an artificial neural network

2018 ◽  
Vol 18 ◽  
pp. 04003
Author(s):  
Alisa Kosach ◽  
Evgeny Kovshov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Traffic ◽  
Non Destructive Testing ◽  
Multidimensional Signal Processing ◽  
Destructive Testing ◽  
Multidimensional Signal ◽  
Artificial Neural ◽  
Processing And Storage ◽  
Non Destructive

The principle of constructing a universal software and hardware platform for the collection, processing and storage of data obtained as a result of non-destructive testing of products is considered. The platform also includes a neural network module for filtering data traffic, which has a wide potential for application: from image and multidimensional signal processing to data classification. The system of remote leakage control, built on the basis of a universal digital platform with artificial neural network, is described.

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