Pulsed thermography inspection of adhesive composite joints: computational simulation model and experimental validation

Grosso, M.; Lopez, Juan E.C.; Silva, Vitor M.A.; Soares, Sergio Damasceno; Rebello, J. M. A.; Pereira, Gabriela Ribeiro

doi:10.1016/j.compositesb.2016.09.011

Cited by 25 publications

(13 citation statements)

References 13 publications

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“…Using Python, a parametrical FEM model was prepared to study the effect that the presence of an internal defect generates on the temperature distribution of a thermally loaded solid. To check that the model worked correctly, a first battery of tests was carried out using the same geometries and materials employed by other authors [ 7 , 9 , 14 ]. Once it was checked that the thermal distribution results of these tests coincided with those from the authors in both shapes and values.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, in this study, a simpler approach was carried out in which the macrostructural thermomechanical behavior of the material was considered as continuum and isotropic. This approach was previously considered in several works [ 7 , 12 , 14 ].…”

Section: Methodsmentioning

confidence: 99%

“…In turn, 3D modelling and simulation methods allow for the generation of ideal surfaces under which geometrical conditions may differ from the real ones. Carvalho et al [ 7 ] apply a model based on FEM simulation to solve the heat transient problem, while other authors apply a surface flux [ 10 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods

Rodríguez‐Martín

Fueyo

Aguilera

et al. 2020

Sensors

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The present article addresses a generation of predictive models that assesses the thickness and length of internal defects in additive manufacturing materials. These modes use data from the application of active transient thermography numerical simulation. In this manner, the raised procedure is an ad-hoc hybrid method that integrates finite element simulation and machine learning models using different predictive feature sets and characteristics (i.e., regression, Gaussian regression, support vector machines, multilayer perceptron, and random forest). The performance results for each model were statistically analyzed, evaluated, and compared in terms of predictive performance, processing time, and outlier sensibility to facilitate the choice of a predictive method to obtain the thickness and length of an internal defect from thermographic monitoring. The best model to predictdefect thickness with six thermal features was interaction linear regression. To make predictive models for defect length and thickness, the best model was Gaussian process regression. However, models such as support vector machines also had significative advantages in terms of processing time and adequate performance for certain feature sets. In this way, the results showed that the predictive capability of some types of algorithms could allow for the detection and measurement of internal defects in materials produced by additive manufacturing using active thermography as a non-destructive test.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods

Rodríguez‐Martín

Fueyo

Aguilera

et al. 2020

Sensors

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“…Infrared thermography (IRT) is used as a non-invasive monitoring technique in several industrial sectors and is effective for defects inspection in the microelectronic packaging industry. This technique has been used as a nondestructive technique in order to avoid unexpected equipment stops [5], [6]. IRT allows the operator to obtain a surface temperature map of any sample, regardless of the geometry, from the thermal energy radiated in the infrared electromagnetic band and from the heat flow [7].…”

Section: Introductionmentioning

confidence: 99%

Active Thermography Data-Processing Algorithm for Nondestructive Testing of Materials

et al. 2020

Self Cite

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This article presents the development of an active thermography algorithm capable of detecting defects in materials, based on the techniques of Thermographic Signal Reconstruction (TSR), Thermal Contrast (TC) and the physical principles of heat transfer. The results obtained from this algorithm are compared to the TSR technique and the raw thermogram obtained by stepped thermography inspection. Experimentally, a short thermal pulse is used and the surface temperature of the sample is monitored over time with an infrared camera. Due to the volume of data, the first step is data compression. Newton's law of cooling was used to store the normalized temperature data pixel-by-pixel over time and a compression ratio of 99% was obtained. The main contributions of the developed algorithm are: only four parameters for data compression and the concept of change in the direction of the heat flow to delimit the edges of the defects, where the borders are identified with a remarkable accuracy. Some well known image processing technique are also integrated to improve the thermal analysis: edge detection/interface between the sample and the image background; consolidation in a single image by aggregating the indicators referring to the concept of cooling/heating time constant, maximum thermal amplitude and contrast. INDEX TERMS active thermography, thermal analysis, infrared imaging, data compression, change detection algorithms, image processing.

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“…Kurpinski et al [ 19 ] proposed the use of the thermographic method to locate defects in adhesive joints and also to assess the adhesive patch continuity between two steel elements. Other examples of evaluation of adhesive joints using the thermographic method also refer to joining metallic components with adhesive [ 20 , 21 ]. Not only images from a thermographic camera were used in them but also computer simulations.…”

Section: Introductionmentioning

confidence: 99%

Glass–Adhesive–Steel Joint Inspection Using Mechanic and High Frequency Electromagnetic Waves

et al. 2020

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The utilization of a glass–adhesive–steel joint in modern machine and vehicle production is constantly growing. Despite the technological regime during the production process, various defects in the adhesive joints may occur. One of the major problems is lack of adhesive between joined materials. Due to the widespread use of non-transparent layers increasing adhesion, it is frequently not possible to conduct simple visual inspections. Hence, it is important to develop a non-destructive adhesive path continuity examination procedure. In that process, the different physical properties of the joint materials must be taken into account. Therefore, in this paper various testing methods were used, including ultrasound, thermographic and electromagnetic methods operating in the microwave and terahertz frequency range. Different physical phenomena of the methods allowed for examination of the joint in a wide context. In order to verify the information brought by each method, the results were transferred into common coordinate space, compared and finally integrated. Various opinion pooling strategies were conducted to fuse data, which allowed us to emphasize convergent and complementary information on adhesive continuity. The obtained results are promising and constitute the basis for further work on an integrated system for automatic evaluation of a wide range of possible defects in glass–adhesive–steel joints.

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Pulsed thermography inspection of adhesive composite joints: computational simulation model and experimental validation

Cited by 25 publications

References 13 publications

Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods

Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods

Active Thermography Data-Processing Algorithm for Nondestructive Testing of Materials

Glass–Adhesive–Steel Joint Inspection Using Mechanic and High Frequency Electromagnetic Waves

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