Acoustic emission testing the F-111

Carlyle, John M.

doi:10.1016/0308-9126(89)90859-6

Cited by 10 publications

(5 citation statements)

References 1 publication

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“…Stone and Krishnamurthy 144 proposed a thrust force controller using an ANN to reduce the development of delaminations caused by the entry and removal of a drill bit to an FRP. Machine learning was employed to classify the Visual inspection [85][86][87][88][89][90] Ultrasonic inspection [91][92][93][94] Eddy current [95][96][97][98][99] Radiography 100-105 (e.g., x-ray) Thermography 97,[106][107][108][109][110] Acoustic emission [111][112][113][114][115] Fiber optic sensors [116][117][118][119][120][121] (e.g., fiber Bragg grating) Piezoelectric transducers [122][123][124][125] Laser vibrometry [126][127][128][129][130][131][132] failure methods of composite plates bolted together. 145 A beneficial application of ML is prediction making.…”

Section: Composite Applications With Machine Learningmentioning

confidence: 99%

The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review

Nelon

Myers

Hall

2022

Journal of Composite Materials

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Machine learning (ML) has emerged as a useful predictive tool based on mathematical and statistical relationships for various engineering problems. The pairing of structural health monitoring (SHM) and nondestructive evaluation (NDE) methods with ML algorithms has yielded beneficial results in addressing the damage state of a material or system. Damage state descriptions addressed with ML include detecting a damage mechanism, locating a mechanism, identifying the type of mechanism, assessing the extent of the damage mechanism, and estimating the useful remaining life of a material or system. Damage evaluation research of composite materials has progressed with the increased usage of composite structural elements in the aerospace industry. NDE methods are a viable candidate for pairing with ML algorithms to improve damage state monitoring of composite materials due to the complexity associated with the structure of composites. Fiber-reinforced polymers (FRP), for example, contain at least two constituent materials a fiber and matrix material whose mechanical behavior and interactions contribute to the performance of an FRP. Unlike conventional composite analytical models that require explicit information about the constituents and microstructure of a laminate, an ML algorithm can construct damage evaluation predictions when employing exclusively past operational performance or data from an SHM or NDE method. A researcher determines the type of data selected when applying an ML model for trend analysis, anomaly detection, or prediction making. However, no one specific input feature is required for utilizing an ML model, and examples of possible data features include material properties, physical dimensions, and collected evaluation data. In the present review, applications of ML combined with the damage state evaluation of composite materials, particularly examining FRPs, are discussed to demonstrate the predictive capabilities of ML and its viability for future applications, especially in industrial environments, to minimize costs and improve damage detection rates.

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Section: Composite Applications With Machine Learningmentioning

confidence: 99%

The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review

Nelon

Myers

Hall

2022

Journal of Composite Materials

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“…Acoustic emission for structural health monitoring (SHM) has been widely used 5 to monitor, for example, industrial oil tanks, 6 pressure vessels 7 and aircraft. 8 Pitting corrosion has been the subject of numerous electrochemical studies. In this work a standard electrochemical potentiostat circuit was used.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission from pitting corrosion in stressed stainless steel plate

Lee¹,

Scholey²,

Worthington³

et al. 2008

Corrosion Engineering, Science and Technology

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The acoustic emission (AE) technique is used to detect and study the AE signals emitted from pitting corrosion on 316L stainless steel plate samples subjected to different levels of surface stress. The tests are performed in a four point bend using accelerated localised salt water corrosion driven by a potentiostat. The AE event rate and the corrosion rate are both found to be affected by the different surface stresses on the plate. After an initial stress dependent phase that lasts around two hours, the AE event rate reduces to a fairly steady rate that is independent of the applied stress. The statistics of the AE data collected from pitting in these specimens is used as the input into a model of an AE corrosion detection system in a larger scale structure. Such a model can be used to estimate the performance of the system as a function of parameters such as threshold level and sensor separation.

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“…AE technique has been applied in a number of areas where are process monitoring and control, material studies, structural integrity assessments, ordnance equipment testing, mechanical equipment monitoring, and leak testing. AE can be a powerful and contemporary tool for detecting crack locations and analyzing crack initiation during the testing [1,2]. AE monitoring has been widely used as a non-destructive testing(NDT) for structural integrity checks of composite materials [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emission Monitoring of Ablative Composite Liner Bonded on Steel Nozzle under Static Loading

Shul

Lee

Kim³

et al. 2006

KEM

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AE technique was applied to the structural strength tests of the ablative composite liner bonded on steel nozzle to figure out the unexpected problem that happened during the flight test. Two different kinds of specimen, which were specimens with some initial crack and without initial crack each, were used for the test. AE test methodology and parameters were investigated to assess the damage of them. NDT using X-ray technique was performed to improve the test reliability before and after each test. The study revealed that AE technique is a good method to evaluate damage on this kind of specimen with better accuracy.

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Acoustic emission testing the F-111

Cited by 10 publications

References 1 publication

The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review

The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review

Acoustic emission from pitting corrosion in stressed stainless steel plate

Acoustic Emission Monitoring of Ablative Composite Liner Bonded on Steel Nozzle under Static Loading

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