Combining eddy current, thermography and laser scanning to characterize low-velocity impact damage in aerospace composite sandwich panels

Rellinger, T.; Underhill, P. R.; Krause, Thomas W.; Wowk, Diane

doi:10.1016/j.ndteint.2021.102421

Cited by 22 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Laser spot thermography has a higher sensitivity to surface and subsurface defects, but low productivity, so it is advisable to use it for additional inspection of product problem areas detected by other inspection methods, or for inspection of small-sized products. In the article by Rellinger T. and co-authors [18], it was shown that laser thermography in combination with other methods is a more reliable method than all currently known ones.…”

Section: Introductionmentioning

confidence: 99%

Using Laser Point Scanning Thermography for Quality Monitoring of Products Made of Composite Materials

Divin,

Karpov,

Zakharov

et al. 2024

Engineering Technologies and Systems

View full text Add to dashboard Cite

Introduction. Control of the presence of subsurface defects in products from composite materials is necessary for verification of products after release from production and in the process of operation. Aim of the Study. The purpose of the presented work is to estimate the parameters of subsurface defects using local laser thermography, suitable for quality control of both small objects and suspicious areas of large objects with curved surfaces. Materials and Methods. The laboratory setup on which this work was carried out includes a robotic arm, a COX CG640 thermal imager and a 3 W laser. The method was tested on a fiberglass sample with introduced delamination defect simulations located at different depths below the surface. By means of computer modeling rational parameters of thermographic control were selected, providing reliable detection of the defect at a depth of up to 3 mm under the surface of the composite sample. Results. Numerical modeling of surface temperature field induced by moving focused laser beam was carried out using COMSOL software package. It showed that laser beam with 3 W power moving at 5 mm/s provided the thermal contrast sufficient to detect the defects at the depth up to 3 mm. The obtained experimental data are in satisfactory agreement with numerical modeling both qualitatively and quantitatively. Experimental data were used to construct a regression model for determining defect depth based on the maximal thermal contrast and the time interval between heating and the contrast maximum. Discussion and Conclusion. The results obtained in this work allow us to propose a technique for detecting defects in fiberglass plastics and estimating their depth. The coefficient of determination for the obtained regression model was found to be equal to 0.95, and the mean square error of the metric was no more than 0.016 mm2. The use of a robotic arm to scan objects will make it possible to investigate objects with complex curved surfaces.

show abstract

“…Eddy current inspection can effectively detect disbonds by analyzing the changes of eddy current and impendence in the coil. 7 However, the main limitation of this technique is only effective for electrically conductive materials. X-ray detection for disbonds can provide accurate results based on the X-ray's penetration, 8 but the lower efficiency is a major disadvantage.…”

Section: Introductionmentioning

confidence: 99%

Disbond detection of honeycomb sandwich structure through laser ultrasonics using signal energy map and local cross-correlation

Dong

Chen

Saito

et al. 2023

Jnl of Sandwich Structures & Materials

View full text Add to dashboard Cite

Detecting disbonds in honeycomb sandwich structures using ultrasonics is challenging due to the complex wave propagation. This paper investigates the method for disbond quantification in honeycomb sandwich structures using laser ultrasonics. In the experiments on an aluminum specimen, the detection frequency range was determined by the local defect resonance (LDR) in the disbond regions. The energy maps of bandpass wavefields identified the locations of disbonds. To improve the results, a frequency-wavenumber analysis of the wavefields was performed. The wavenumber spectra were reconstructed by wavenumber filtering to strengthen the outline components for quantifying disbonds. Local cross-correlation (LCC) was proposed to show the outlines of disbonds and honeycomb cells, whose results agreed well with the X-ray imaging. Moreover, LCC could also quantify a disbond in the composite honeycomb sandwich structure. Consequently, combining energy maps and LCC results can effectively detect the locations and sizes of disbonds in honeycomb sandwich structures.

show abstract

Combining eddy current, thermography and laser scanning to characterize low-velocity impact damage in aerospace composite sandwich panels

Cited by 22 publications

References 23 publications

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

Using Laser Point Scanning Thermography for Quality Monitoring of Products Made of Composite Materials

Disbond detection of honeycomb sandwich structure through laser ultrasonics using signal energy map and local cross-correlation

Contact Info

Product

Resources

About