The Application of Automated Ultrasonic Inspection for Damage Detection and Evaluation in Composite Materials

1991 ◽  
pp. 415-423
Author(s):  
F. De Roey ◽  
D. Van Hemelrijck ◽  
L. Schillemans ◽  
I. Daerden ◽  
F. Boulpaep ◽  
...  
Keyword(s):  
Composite Materials ◽  
Damage Detection ◽  
Ultrasonic Inspection

The Summary on Important Damage Detection Technology of Composite Materials

2014 ◽  
Vol 1055 ◽  
pp. 32-37
Author(s):  
Tian Jiao Liu ◽  
Wei Fang Zhang ◽  
Xiao Liang Fang
Keyword(s):  
Composite Materials ◽  
Damage Detection ◽  
Composite Structure ◽  
Ultrasonic Inspection ◽  
Structure Defects ◽  
Testing Method ◽  
Detection Technology ◽  
Radiographic Inspection ◽  
Speckle Detection ◽  
Application Specific

It mainly introduces four methods, permeation testing method, radiographic inspection, ultrasonic inspection and measurement and holographic speckle detection, to test composite structure defects, including their testing principles, field of application, specific testing steps,etc. Among the four nondestructive testing technologies for composite materials, the holographic speckle detection is the most advanced and widely used one and has been being improved.

Development of high-speed optical wavelength interrogation system for damage detection in composite materials

2005 ◽  
Author(s):  
Shinji Komatsuzaki ◽  
Seiji Kojima ◽  
Akihito Hongo ◽  
Nobuo Takeda ◽  
Takeo Sakurai
Keyword(s):  
Composite Materials ◽  
Damage Detection ◽  
High Speed ◽  
Optical Wavelength

scholarly journals Inspection of the Composite Materials

2021 ◽  
Vol 10 (3) ◽  
pp. 146
Author(s):  
Yaser A. Jasim ◽  
Senan Thabet ◽  
Thabit H. Thabit
Keyword(s):  
Composite Materials ◽  
Visual Inspection ◽  
Magnetic Particle ◽  
Phased Array ◽  
Ultrasonic Inspection ◽  
Test Method ◽  
Main Method ◽  
Destructive Test ◽  
Eddy Current Inspection ◽  
Non Destructive

<p><em>A non-destructive test method is the main method to examine most of the materials, composite materials in particular. There are too many </em><em>Non-Destructive Test (</em><em>NDT) methods to inspect the materials such as, Visual Inspection, Liquid Penetrate Inspection, Eddy-Current Inspection, Phased Array Inspection, Magnetic Particle Inspection and Ultrasonic Inspection</em><em>.</em></p><p><em>This paper aims to creat a unified methodology for engineers depending on reaserch onion to study the inspection of the composite materials.</em></p><p><em>The researchers concluded that NDT method is the most suitable method for testing any materials and the composite materials. They also recommended to choose the most suitable NDT method as every materials and composite materials have its own properties as well as the inspection methods had its own capabilities and limitations. </em></p>

Damage Detection of Composite Materials Using Data Fusion With Deep Neural Networks

2020 ◽  
Author(s):  
Shweta Dabetwar ◽  
Stephen Ekwaro-Osire ◽  
João Paulo Dias
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Composite Materials ◽  
Data Fusion ◽  
Damage Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Mixed Data ◽  
Image Encoding ◽  
Using Data

Abstract Composite materials have enormous applications in various fields. Thus, it is important to have an efficient damage detection method to avoid catastrophic failures. Due to the existence of multiple damage modes and the availability of data in different formats, it is important to employ efficient techniques to consider all the types of damage. Deep neural networks were seen to exhibit the ability to address similar complex problems. The research question in this work is ‘Can data fusion improve damage classification using the convolutional neural network?’ The specific aims developed were to 1) assess the performance of image encoding algorithms, 2) classify the damage using data from separate experimental coupons, and 3) classify the damage using mixed data from multiple experimental coupons. Two different experimental measurements were taken from NASA Ames Prognostic Repository for Carbon Fiber Reinforced polymer. To use data fusion, the piezoelectric signals were converted into images using Gramian Angular Field (GAF) and Markov Transition Field. Using data fusion techniques, the input dataset was created for a convolutional neural network with three hidden layers to determine the damage states. The accuracies of all the image encoding algorithms were compared. The analysis showed that data fusion provided better results as it contained more information on the damages modes that occur in composite materials. Additionally, GAF was shown to perform the best. Thus, the combination of data fusion and deep neural network techniques provides an efficient method for damage detection of composite materials.

The significance of damage and defects and their detection in composite materials: A review

1992 ◽  
Vol 27 (1) ◽  
pp. 29-42 ◽  
Author(s):  
W J Cantwell ◽  
J Morton
Keyword(s):  
Composite Materials ◽  
Damage Detection ◽  
Failure Modes ◽  
Fracture Mechanisms ◽  
Reinforced Composites ◽  
Load Bearing ◽  
Fibre Composites ◽  
Non Destructive ◽  
Fibre Reinforced Composites ◽  
Non Destructive Techniques

In this paper the various failure modes which occur in long fibre composites are described and discussed. The significance of each of these fracture mechanisms, in terms of their energy-dissipating capacity as well as their effect on the residual load-bearing properties, is considered. A brief review of both the destructive and non-destructive techniques used for detecting and characterizing defects and damage is presented. The ability of each technique to identify the various fracture mechanisms involved in the failure of long fibre reinforced composites is discussed and their overall suitability for damage detection evaluated.

scholarly journals Modal-based vibrothermography using feature extraction with application to composite materials

2019 ◽  
Vol 19 (4) ◽  
pp. 967-986 ◽  
Author(s):  
Xintian Chi ◽  
Dario Di Maio ◽  
Nicholas AJ Lieven
Keyword(s):  
Feature Extraction ◽  
Composite Materials ◽  
Damage Detection ◽  
Infrared Camera ◽  
Experimental Tests ◽  
Detection Algorithm ◽  
Modal Testing ◽  
Theoretical Development ◽  
Operational Environment ◽  
Practical Applications

This research focuses on the development of a damage detection algorithm based on modal testing, vibrothermography, and feature extraction. The theoretical development of mathematical models is presented to illustrate the principles supporting the associated algorithms, through which the importance of the three components contributing to this approach is demonstrated. Experimental tests and analytical simulations have been performed in laboratory conditions to show that the proposed damage detection algorithm is able to detect, locate, and extract the features generated due to the presence of sub-surface damage in aerospace grade composite materials captured by an infrared camera. Through tests and analyses, the reliability and repeatability of this damage detection algorithm are verified. In the concluding observations of this article, suggestions are proposed for this algorithm’s practical applications in an operational environment.

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