Neural Network Nondestructive Evaluation of Composite Structures from Acoustic Emission Data

2013 ◽  
pp. 313-342
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Nondestructive Evaluation ◽  
Composite Structures ◽  
Emission Data

An Overview: New Ideas in Nondestructive Evaluation

1981 ◽  
Vol 54 (5) ◽  
pp. 996-1002 ◽  
Author(s):  
Harold Berger
Keyword(s):  
Acoustic Emission ◽  
Nondestructive Evaluation ◽  
Composite Structures ◽  
Eddy Currents ◽  
Raw Materials ◽  
Tomographic Imaging ◽  
X Rays ◽  
Pulsed Eddy Currents ◽  
New Ideas ◽  
Materials Used

Abstract It is recognized that present methods for NDE of tires emphasize x-radiography, ultrasonics, and holographic interferometry. Modifications of these approaches in terms of improved signal processing, image magnification (for x rays), electronic focus (for ultrasonics), and tomographic imaging appear to offer promise for improved NDE of tires. In addition, techniques such as acoustic emission, pulsed eddy currents, microwaves, and vibrothermography also show promise. The suggestion that other NDE methods now being investigated for application to composite structures be followed closely appears valid as does the concept of improved procedures to verify the condition of raw materials used in initial fabrication.

scholarly journals Prediction of fiber breakage and matrix cracking in polymeric composites under low-cycle fatigue regimes by fuzzy and wavelet clustering of acoustic emission signals

2021 ◽  
Author(s):  
Sattar Mohammadi Esfarjani ◽  
Mohammad Azadi ◽  
Mohsen Alizadeh ◽  
Hassan Sayar
Keyword(s):  
Acoustic Emission ◽  
Composite Structures ◽  
Mechanical Test ◽  
Low Cycle Fatigue ◽  
Polymeric Composites ◽  
Matrix Cracking ◽  
Fiber Breakage ◽  
Cycle Fatigue ◽  
Emission Data ◽  
Pull Out

Abstract One of methods for detecting cracks and estimating their growth in materials such as composites is the acoustic emission technique. The detection of damages, cracks and their growth in industrial composite structures, under static and dynamic loads, has a significant importance, in order to prevent any damages and increase the reliability. Therefore, achieving required technical knowledge in this field, can be helpful in repairing and the maintenance of the part in industries. The prediction of the damage in polymeric composites under static loads has been already investigated by researchers; however, under cyclic loadings, researches about this behavior are still rare. In this study, by acoustic emission sensors and analyzing experimental data, the damage, including matrix cracking, the fiber breakage and other damages (debonding, fiber pull-out and delamination) during dynamic loading was investigated. At the first stage, standard specimens were made by the pure resin epoxy and the pure carbon fiber, subjected to monotonic tensile loading and then, the frequency of the failure was extracted. Then, composite specimens were loaded in the low-cycle fatigue regime. Mechanical test results and acoustic emission data were analyzed by fuzzy C-Means and wavelet transform methods and then compared to each other to find the percentage of failures in first, mid- and last cycles by the differentiation of failure types. Results clearly indicated that the acoustic emission approach is useful and an effective tool for identifying and detecting damages in polymeric composites.

scholarly journals Health Monitoring of Aerospace Structures Utilizing Novel Health Indicators Extracted from Complex Strain and Acoustic Emission Data

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5701
Author(s):  
Georgios Galanopoulos ◽  
Dimitrios Milanoski ◽  
Agnes Broer ◽  
Dimitrios Zarouchas ◽  
Theodoros Loutas
Keyword(s):  
Acoustic Emission ◽  
Health Monitoring ◽  
Composite Structures ◽  
Phased Array ◽  
Essential Feature ◽  
Health Indicators ◽  
Sensor Data ◽  
Composite Panel ◽  
Emission Data ◽  
Variable Amplitude

The development of health indicators (HI) of diagnostic and prognostic potential from generally uninformative raw sensor data is both a challenge and an essential feature for data-driven diagnostics and prognostics of composite structures. In this study, new damage-sensitive features, developed from strains acquired with Fiber Bragg Grating (FBG) and acoustic emission (AE) data, were investigated for their suitability as HIs. Two original fatigue test campaigns (constant and variable amplitude) were conducted on single-stringer composite panels using appropriate sensors. After an initial damage introduction in the form of either impact damage or artificial disbond, the panels were subjected to constant and variable amplitude compression–compression fatigue tests. Strain sensing using FBGs and AE was employed to monitor the damage growth, which was further verified by phased array ultrasound. Several FBGs were incorporated in special SMARTapesTM, which were bonded along the stiffener’s feet to measure the strain field, whereas the AE sensors were strategically placed on the panels’ skin to record the acoustic emission activity. HIs were developed from FBG and AE raw data with promising behaviors for health monitoring of composite structures during service. A correlation with actual damage was attempted by leveraging the measurements from a phased array camera at several time instances throughout the experiments. The developed HIs displayed highly monotonic behaviors while damage accumulated on the composite panel, with moderate prognosability.

scholarly journals Deep Neural Network Tool Chatter Model for Aluminum Surface Milling Using Acoustic Emmision Sensor

2018 ◽  
Vol 217 ◽  
pp. 03003
Author(s):  
M. Abul Hasan ◽  
Muhamad-Husaini Abu-Bakar ◽  
Rizal Razuwan ◽  
Zainal Nazri
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Monitoring System ◽  
Deep Neural Network ◽  
Machining Process ◽  
Aluminum Surface ◽  
Emission Data ◽  
Chatter Identification ◽  
Tool Monitoring ◽  
Identification Model

Chatter is a self-excited vibration in any machining processes which contributes to the system instability due to resonance and resulting in an inaccuracy in machining product. Due to demand for a high precision product, industries are nowadays moving towards implementing a tool monitoring system as a feedback. Currently, an electromagnetic sensor was used to detect chatter in tools, but this sensor introduces a drawback such as bulky in size, sensitive to noise and not suitable to be implemented in the small machining center. This paper aims to propose a chatter identification model for face milling tool based on acoustic emission data for tool monitoring system. Acoustic emission data is collected at four level of cutting depth in milling with linear tool path movement on aluminum T6 6061 materials. the Deep Neural Network (DNN) model was developed using multiple deep-learning frameworks for the chatter detection system. This model approach shows a good agreement with experimental data with 4% error. As a conclusion, the DNN chatter identification model was successfully developed for the aluminum milling process applications. This finding is essential for anomaly detection during machining process and able to suggest for a better machining parameter for the aluminum machining process.

scholarly journals Prediction of fiber breakage and matrix cracking in polymeric composites under low-cycle fatigue regimes by fuzzy and wavelet clustering of acoustic emission signals

2021 ◽  
Author(s):  
Sattar Mohammadi Esfarjani ◽  
Mohammad Azadi ◽  
Mohsen Alizadeh ◽  
Hassan Sayar
Keyword(s):  
Acoustic Emission ◽  
Composite Structures ◽  
Mechanical Test ◽  
Low Cycle Fatigue ◽  
Polymeric Composites ◽  
Matrix Cracking ◽  
Fiber Breakage ◽  
Cycle Fatigue ◽  
Emission Data ◽  
Pull Out

Abstract One of methods for detecting cracks and estimating their growth in materials such as composites is the acoustic emission technique. The detection of damages, cracks and their growth in industrial composite structures, under static and dynamic loads, has a significant importance, in order to prevent any damages and increase the reliability. Therefore, achieving required technical knowledge in this field, can be helpful in repairing and the maintenance of the part in industries. The prediction of the damage in polymeric composites under static loads has been already investigated by researchers; however, under cyclic loadings, researches about this behavior are still rare. In this study, by acoustic emission sensors and analyzing experimental data, the damage, including matrix cracking, the fiber breakage and other damages (debonding, fiber pull-out and delamination) during dynamic loading was investigated. At the first stage, standard specimens were made by the pure resin epoxy and the pure carbon fiber, subjected to monotonic tensile loading and then, the frequency of the failure was extracted. Then, composite specimens were loaded in the low-cycle fatigue regime. Mechanical test results and acoustic emission data were analyzed by fuzzy C-Means and wavelet transform methods and then compared to each other to find the percentage of failures in first, mid- and last cycles by the differentiation of failure types. Results clearly indicated that the acoustic emission approach is useful and an effective tool for identifying and detecting damages in polymeric composites.

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