Application of the Pulse Infrared Thermography Method for Nondestructive Evaluation of Composite Aircraft Adhesive Joints

Kostroun, Tomáš; Dvořák, Milan

doi:10.3390/ma14030533

Cited by 12 publications

(5 citation statements)

References 18 publications

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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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“…Lock-in thermography is a popular type of non-destructive testing that uses an external source to induce temperature changes in a material or component, and then uses an infrared camera to measure the resulting thermal response. Lock-in thermography is often used to inspect composite materials such as aerospace structural elements [ 1 , 2 , 3 , 4 , 5 ], wind turbine blade parts [ 6 , 7 , 8 ], and other advanced composites such as composites containing nanotubes [ 9 , 10 ] as well as sandwich structures [ 11 ]. Lock-in thermography is widely used for detecting subsurface defects, such as cracks [ 12 , 13 ], delamination [ 14 , 15 , 16 ], impact damage [ 17 , 18 ], and corrosion [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Excitation Periods on the Outcome of Lock-In Thermography

et al. 2023

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Thermal imaging is a non-destructive test method that uses an external energy source, such as a halogen lamp or flash lamp, to excite the material under test and measure the resulting temperature distribution. One of the important parameters of lock-in thermography is the number of excitation periods, which is used to calculate a phase image that shows defects or inhomogeneities in the material. The results for multiple periods can be averaged, which leads to noise suppression, but the use of a larger number of periods may cause an increase in noise due to unsynchronization of the camera and the external excitation source or may lead to heating and subsequent damage to the sample. The phase image is the most common way of representing the results of lock-in thermography, but amplitude images and complex images can also be obtained. In this study, eight measurements were performed on different samples using a thermal pulse source (flash lamp and halogen lamp) with a period of 120 s. For each sample, five phase images were calculated using different number of periods, preferably one to five periods. The phase image calculated from one period was used as a reference. To determine the effect of the number of excitation periods on the phase image, the reference phase image for one period was compared with the phase images calculated using multiple periods using the structural similarity index (SSIM) and multi-scale SSIM (MS-SSIM).

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“…Despite this, the development of Industry 4.0 comes with changes in terms of the user-equipment interface, and any change involves the unknown, which can bring about reluctance or lack of confidence in the user in the displayed result. Digitalization comes primarily with the improvement and development of new processes and methods, but it also comes with the disposal of human errors, especially in the field of NDT methods where the final interpretation and final decision involves the human factor [5][6][7]. NDT, which stands for the term "nondestructive testing", does not affect the surface quality of the parts, the material, or the geometry of the tested components; this term is found in various publications, books, and media, and it is known as NDT, NDI, or NDE using different endings (e.g., testing, inspections, examinations, evaluations).…”

Section: Introductionmentioning

confidence: 99%

“…The magnetic particle method is the method most often used to detect microdefects (usually linear cracks) in the case of materials with ferromagnetic properties. The principle of examination with this method is quite simple, namely that for the cylindrical parts, standard control equipment is used, with a central conductor mounted, through which electric current passes [5,7]. The parts are magnetized, and then the applied magnetic particles are attracted to the defective areas; however, it is important that the orientation of the defects is about 90 degrees from the direction of the field lines, as this is one of its basic characteristic methods [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Particle Inspection Optimization Solution within the Frame of NDT 4.0

2021

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The quality of product and process is one of the most important factors in achieving constructively and then functionally safe products in any industry. Over the years, the concept of Industry 4.0 has emerged in all the quality processes, such as nondestructive testing (NDT). The most widely used quality control methods in the industries of mechanical engineering, aerospace, and civil engineering are nondestructive methods, which are based on inspection by detecting indications, without affecting the surface quality of the examined parts. Over time, the focus has been on research with the fourth generation in nondestructive testing, i.e., NDT 4.0 or Smart NDT, as a main topic to ensure the efficiency and effectiveness of the methods for a safe detection of all types of discontinuities. This area of research aims at the efficiency of methods, the elimination of human errors, digitalization, and optimization from a constructive point of view. In this paper, we presented a magnetic particles inspection method and the possible future directions for the development of standard equipment used in the context of this method in accordance with the applicable physical principles and constraints of the method for cylindrical parts. A possible development direction was presented in order to streamline the mass production of parts made of ferromagnetic materials. We described the methods of analysis and the tools used for the development of a magnetic particle inspection method used for cylindrical parts in all types of industry and NDT 4.0; the aim is to provide new NDT 4.0 directions in optimizing the series production for cylindrical parts from industry, as given in the conclusion of this article.

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Application of the Pulse Infrared Thermography Method for Nondestructive Evaluation of Composite Aircraft Adhesive Joints

Cited by 12 publications

References 18 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

The Impact of Excitation Periods on the Outcome of Lock-In Thermography

Magnetic Particle Inspection Optimization Solution within the Frame of NDT 4.0

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