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This study introduces and evaluates a new approach to reconstruct image sequences acquired during non-destructive testing by pulsed thermography. The proposed method consists of applying two linear support vector regressions to model the evolution of the data from both a spatial and temporal point of view. Each regression vectors will map the data with the number of pixels and the number of frames using convex optimisation. Then the regression vectors are used to predict a more robust representation of the data, thus reconstructing the sequence. The proposed method has been applied to data related to a reference sample of carbon reinforced fibre with known defects. This approach was evaluated on a sequence with severe non-uniform heating and was compared with state-of-the-art methods. Despite being sensitive to nonuniform heating, the proposed method provided a higher CNR score on smaller defects, compared with state-of-the-art methods. For the shallowest defects it shows better performance in term of contrast reconstruction compared to partial least-squares thermography (PLST). It also outperforms principal component thermography (PCT), and thermographic signal reconstruction-PCT (TSR-PCT) for defects located at a depth of 0.6 mm and 0.8 mm below the surface.

Active microwave thermography (AMT) is a thermographic nondestructive testing and evaluation techniquethat utilizes an electromagnetic-based excitation with a subsequent infrared measurement of the surface thermal profile of the material or structure of interest. AMT has been successfully applied to several aerospace and civil infrastructure applications. This work seeks to expand the performance of AMT by incorporating a signal processing technique common to traditional (flash-lamp) thermography, referred to as pulsed thermography (PT). PT operates on the premise of a pulsed excitation, as opposed to a constant or step excitation (ST) over a given time-period that is typical to traditional active thermography. This work applies the pulsed approach to AMT, herein referred to as P-AMT, and compares the thermal contrast (TC) and signal-to-noise ratio (SNR) of traditional and pulsed AMT inspections as applied to a moisture ingress detection need. The results suggest that the optimal heating time (indicated through SNR) for P-AMT is less than that of traditional AMT with a reduced overall (absolute) temperature. This is important as it relates to any inspection with concerns for thermal damage as well an overall reduction in required inspection time.

This study introduces and evaluates a new approach to reconstruct image sequences acquired during non-destructive testing by pulsed thermography. The proposed method consists of applying two linear support vector regressions to model the evolution of the data from both a spatial and temporal point of view. Each regression vectors will map the data with the number of pixels and the number of frames using convex optimisation. Then the regression vectors are used to predict a more robust representation of the data, thus reconstructing the sequence. The proposed method has been applied to data related to a reference sample of carbon reinforced fibre with known defects. This approach was evaluated on a sequence with severe non-uniform heating and was compared with state-of-the-art methods. Despite being sensitive to nonuniform heating, the proposed method provided a higher CNR score on smaller defects, compared with state-of-the-art methods. For the shallowest defects it shows better performance in term of contrast reconstruction compared to partial least-squares thermography (PLST). It also outperforms principal component thermography (PCT), and thermographic signal reconstruction-PCT (TSR-PCT) for defects located at a depth of 0.6 mm and 0.8 mm below the surface.

Active microwave thermography (AMT) is a thermographic nondestructive testing and evaluation techniquethat utilizes an electromagnetic-based excitation with a subsequent infrared measurement of the surface thermal profile of the material or structure of interest. AMT has been successfully applied to several aerospace and civil infrastructure applications. This work seeks to expand the performance of AMT by incorporating a signal processing technique common to traditional (flash-lamp) thermography, referred to as pulsed thermography (PT). PT operates on the premise of a pulsed excitation, as opposed to a constant or step excitation (ST) over a given time-period that is typical to traditional active thermography. This work applies the pulsed approach to AMT, herein referred to as P-AMT, and compares the thermal contrast (TC) and signal-to-noise ratio (SNR) of traditional and pulsed AMT inspections as applied to a moisture ingress detection need. The results suggest that the optimal heating time (indicated through SNR) for P-AMT is less than that of traditional AMT with a reduced overall (absolute) temperature. This is important as it relates to any inspection with concerns for thermal damage as well an overall reduction in required inspection time.

A quantitative evaluation of delaminations in composite laminates encounters multiple difficulties due to the anisotropic behaviour of the laminate. Extensive calibrations for each structure are required and each depth needs certain manual modifications for optimal performance of the estimation routines. In this manuscript, a robust technique is developed using a numerical model to estimate the thermal diffusivity through the anisotropic material which improves defect depth estimation between each layer. Three different calibration depths are necessary to compute the diffusivity through a certain stacking sequence of a multi-layered composite laminate. The results are compared with the state-of-the-arts experimental evaluation techniques and with a regular numerical model. It is seen that especially for deeper defects, the optimised numerical model delivers more accurate results due to the considered anisotropic diffusivity.

Infrared thermography (IT) is a safe non-destructive evaluation technique that has a fast inspection rate and is generally contactless. It is used for diagnostics and monitoring in several fields including composite materials. In this paper carbon fiber-reinforced polymer plates submitted to tensile testing are inspected using IT. More specifically, carbon/polyether ether ketone panels made of random-oriented strands by compression moulding are submitted to tensile testing and then inspected using three different IT active approaches. The first two approaches use optical sources however with different scanning modes. The first active approach tested is a static surface scanning inspection in reflection mode. The second one is a dynamic line scanning technique where the energy source and camera are in movement with regards to the test sample. The last active IT approach tested uses a mechanical source (ultrasound excitation) to transfer heat to the sample being tested. This last approach is commonly called vibrothermography. Results obtained were then compared to results obtained by micro computed tomography inspection and microscopy. Results revealed voids associated with resin-rich regions as well as cracks.

In this article, optical excitation thermographic techniques, including pulsed thermography and lock-in thermography, were used to detect foreign object defect (FOD) and delamination in CFRP. Then, vibrothermography as an ultrasonic excitation technique was used to detect these defects for the comparative purposes. Different image processing methods, including cold image subtraction (CIS), principal component thermography (PCT), thermographic signal reconstruction (TSR) and Fourier transform (FT), were performed. Finally, a comparison of optical excitation thermography and vibrothermography was conducted, and a thermographic probability of detection was given.

The durability of an exterior finish is affected by the characteristics of the wood. Satisfactory finish life is usually more difficult to achieve on woods of higher density. All wood shrinks as it loses moisture and swells as it absorbs moisture, but some species are more stable than others. Species that shrink and swell the most cause more stress on paint films than woods that are more stable [1]. To this end, let us recall that a painting on wood can be considered as a layered structure: The wood support is coated with a number of superposed priming layers made from mixtures of gesso and glue. A frequent fault resulting from such a system is the formation of detached regions inside the layered structure caused by the shrinkage process of the wood support [2]. Obviously, wood deteriorates more rapidly in warm, humid regions with respect to cool or dry places [3]. The influence of wood conditions on surface coatings is a critical point that should be monitored and that depends on environmental parameters such as microclimate. To prevent and control the effects, keeping costs down, a non-destructive monitoring of wood support behavior under thermal stress is needed. In this work, an integrated approach based on traditional and innovative (HI, PT and NIR) techniques was conducted on a primed support of poplar wood with a complex-shape surface containing areas of artificial defects at several depths due to the influence of the support on the various layers. The obtained results could be arranged, if integrated into a multidisciplinary approach, in order to define and design the conservation of the wooden artifacts.

A quantitative evaluation of delaminations in composite laminates encounters multiple difficulties due to the anisotropic behaviour of the laminate. Extensive calibrations for each structure are required and each depth needs certain manual modifications for optimal performance of the estimation routines. In this manuscript, a robust technique is developed using a numerical model to estimate the thermal diffusivity through the anisotropic material which improves defect depth estimation between each layer. Three different calibration depths are necessary to compute the diffusivity through a certain stacking sequence of a multi-layered composite laminate. The results are compared with the state-of-the-arts experimental evaluation techniques and with a regular numerical model. It is seen that especially for deeper defects, the optimised numerical model delivers more accurate results due to the considered anisotropic diffusivity.

Several quantitative and qualitative methods have been reported in pulsed thermography to analyze and detect defects in materials. Such techniques seek to characterize and predict their depth, size, and thermal properties. Nevertheless, most of the methods need an empirical definition of sound area and are based on a pixel-by-pixel time domain analysis that ignores spatial correlation. In this work, using a technique of decomposition of images in a 2D orthogonal space, we proposed a method to evaluate defects in composite materials and to remove thermal diffusion non-uniformities. We compare two representative time-resolved methods such as TSR and NTC with the new approach by analyzing and inspecting three samples of anisotropic materials with Teflon subsurface insertions. The proposed technique removes the background noise (non-uniform thermal distribution) and enhances the SNR of the defects faster than other techniques. Our method estimates defect’s depths as well up to 1.2 mm in CFRP and GFRP specimens without defining a sound area. An algorithm is also designed to generate automatically an image that estimates the position and depths of the defects. An implementation with a graphical user interface (GUI) of the proposed method in Matlab is available at https://github.com/charlielito/2DOrthogonal_Polynomial_Decomposition

Several quantitative and qualitative methods have been reported in pulsed thermography to analyze and detect defects in materials. Such techniques seek to characterize and predict their depth, size, and thermal properties. Nevertheless, most of the methods need an empirical definition of sound area and are based on a pixel-by-pixel time domain analysis that ignores spatial correlation. In this work, using a technique of decomposition of images in a 2D orthogonal space, we proposed a method to evaluate defects in composite materials and to remove thermal diffusion non-uniformities. We compare two representative time-resolved methods such as TSR and NTC with the new approach by analyzing and inspecting three samples of anisotropic materials with Teflon subsurface insertions. The proposed technique removes the background noise (non-uniform thermal distribution) and enhances the SNR of the defects faster than other techniques. Our method estimates defect’s depths as well up to 1.2 mm in CFRP and GFRP specimens without defining a sound area. An algorithm is also designed to generate automatically an image that estimates the position and depths of the defects. An implementation with a graphical user interface (GUI) of the proposed method in Matlab is available at https://github.com/charlielito/2DOrthogonal_Polynomial_Decomposition

Several quantitative and qualitative methods have been reported in pulsed thermography to analyze and detect defects in materials. Such techniques seek to characterize and predict their depth, size, and thermal properties. Nevertheless, most of the methods need an empirical definition of sound area and are based on a pixel-by-pixel time domain analysis that ignores spatial correlation. In this work, using a technique of decomposition of images in a 2D orthogonal space, we proposed a method to evaluate defects in composite materials and to remove thermal diffusion non-uniformities. We compare two representative time-resolved methods such as TSR and NTC with the new approach by analyzing and inspecting three samples of anisotropic materials with Teflon subsurface insertions. The proposed technique removes the background noise (non-uniform thermal distribution) and enhances the SNR of the defects faster than other techniques. Our method estimates defect’s depths as well up to 1.2 mm in CFRP and GFRP specimens without defining a sound area. An algorithm is also designed to generate automatically an image that estimates the position and depths of the defects. An implementation with a graphical user interface (GUI) of the proposed method in Matlab is available at https://github.com/charlielito/2DOrthogonal_Polynomial_Decomposition

In this article, optical excitation thermographic techniques, including pulsed thermography and lock-in thermography, were used to detect foreign object defect (FOD) and delamination in CFRP. Then, vibrothermography as an ultrasonic excitation technique was used to detect these defects for the comparative purposes. Different image processing methods, including cold image subtraction (CIS), principal component thermography (PCT), thermographic signal reconstruction (TSR) and Fourier transform (FT), were performed. Finally, a comparison of optical excitation thermography and vibrothermography was conducted, and a thermographic probability of detection was given.

Infrared thermography (IT) is a safe non-destructive evaluation (NDE) technique that has a fast inspection rate and is generally contactless. It is used for diagnostics and monitoring in several fields including composite materials. In this paper carbon fiber-reinforced polymer (CFRP) plates submitted to tensile testing are inspected using IT. More specifically, carbon/PEEK (Polyether ether ketone) panels made of random-oriented strands (ROS) by compression moulding are submitted to tensile testing and then inspected using three different IT active approaches. The first two approaches use optical sources however with different scanning modes. The first active approach tested is a static surface scanning inspection in reflection mode. The second one is a dynamic line scanning technique where the energy source and camera are in movement with regards to the test sample. The last active IT approach tested uses a mechanical source (ultrasound excitation) to transfer heat to the sample being tested. This last approach is commonly called vibrothermography (VT). Results obtained were then compared to results obtained by micro computed tomography (µCT) inspection and microscopy. Results revealed voids associated with resin-rich regions as well as cracks.

After the detection of internal defects in materials, the characterization of these plays a decisive role in order to establish the severity of these flaws. Finite difference thermal contrast (FDTC) is a new technique proposed recently for contrast enhancement in sequences of thermal images in order to allow the detection of internal flaws in composite slabs with greater probability of success. Besides FDTC, a criterion was also conceived for the estimation of the depth of the detected defects, which brings good results for shallow and strong contrast defects, but poor estimations for deeper and weaker defects. Considering this problem, a revision of the original criterion is carried out in this paper to define a new and robust criterion for estimating the depth of defects, applied after FDTC en-hancement and flaws detection. Results of the execution of the revised algorithm on a synthetized thermal sequence from an artificial CFRP slab (using ThermoCalc6L software) show a better performance of the estimation task, reducing the average relative error by more than half., Posterior a la detección de defectos internos en los materiales, su caracterización juega un papel decisivo para establecer la seve-ridad de dichas fallas. El contraste térmico por diferencias finitas (CTDF) es una técnica novedosa propuesta recientemente para el mejoramiento del contraste en secuencias de imágenes térmicas que permite la detección de fallas internas en láminas de material compuesto con mayores probabilidades de éxito. A la par con el CTDF, se concibió un criterio de estimación de la profundidad de estos defectos que, aunque brinda buenos resultados para aquellos superficiales y más contrastados térmicamente, pierde calidad en la estimación de la profundidad de defectos más profundos y más débiles en su contraste térmico. Considerando este problema, en este artículo se adelanta una revisión de dicho criterio con el ánimo de definir un método más robusto para el cálculo de pro-fundidad de los defectos contrastados por la técnica CTDF. Los resultados de la ejecución de este nuevo algoritmo sobre imágenes sintéticas generadas a partir de una lámina artificial de CFRP (mediante el software ThermoCalc6L) muestran un mejor desempeño en la estimación, reduciendo el error relativo promedio a más de la mitad.

After the detection of internal defects in materials, the characterization of these plays a decisive role in order to establish the severity of these flaws. Finite difference thermal contrast (FDTC) is a new technique proposed recently for contrast enhancement in sequences of thermal images in order to allow the detection of internal flaws in composite slabs with greater probability of success. Besides FDTC, a criterion was also conceived for the estimation of the depth of the detected defects, which brings good results for shallow and strong contrast defects, but poor estimations for deeper and weaker defects. Considering this problem, a revision of the original criterion is carried out in this paper to define a new and robust criterion for estimating the depth of defects, applied after FDTC en-hancement and flaws detection. Results of the execution of the revised algorithm on a synthetized thermal sequence from an artificial CFRP slab (using ThermoCalc6L software) show a better performance of the estimation task, reducing the average relative error by more than half., Posterior a la detección de defectos internos en los materiales, su caracterización juega un papel decisivo para establecer la seve-ridad de dichas fallas. El contraste térmico por diferencias finitas (CTDF) es una técnica novedosa propuesta recientemente para el mejoramiento del contraste en secuencias de imágenes térmicas que permite la detección de fallas internas en láminas de material compuesto con mayores probabilidades de éxito. A la par con el CTDF, se concibió un criterio de estimación de la profundidad de estos defectos que, aunque brinda buenos resultados para aquellos superficiales y más contrastados térmicamente, pierde calidad en la estimación de la profundidad de defectos más profundos y más débiles en su contraste térmico. Considerando este problema, en este artículo se adelanta una revisión de dicho criterio con el ánimo de definir un método más robusto para el cálculo de pro-fundidad de los defectos contrastados por la técnica CTDF. Los resultados de la ejecución de este nuevo algoritmo sobre imágenes sintéticas generadas a partir de una lámina artificial de CFRP (mediante el software ThermoCalc6L) muestran un mejor desempeño en la estimación, reduciendo el error relativo promedio a más de la mitad.

After the detection of internal defects in materials, the characterization of these plays a decisive role in order to establish the severity of these flaws. Finite difference thermal contrast (FDTC) is a new technique proposed recently for contrast enhancement in sequences of thermal images in order to allow the detection of internal flaws in composite slabs with greater probability of success. Besides FDTC, a criterion was also conceived for the estimation of the depth of the detected defects, which brings good results for shallow and strong contrast defects, but poor estimations for deeper and weaker defects. Considering this problem, a revision of the original criterion is carried out in this paper to define a new and robust criterion for estimating the depth of defects, applied after FDTC en-hancement and flaws detection. Results of the execution of the revised algorithm on a synthetized thermal sequence from an artificial CFRP slab (using ThermoCalc6L software) show a better performance of the estimation task, reducing the average relative error by more than half., Posterior a la detección de defectos internos en los materiales, su caracterización juega un papel decisivo para establecer la seve-ridad de dichas fallas. El contraste térmico por diferencias finitas (CTDF) es una técnica novedosa propuesta recientemente para el mejoramiento del contraste en secuencias de imágenes térmicas que permite la detección de fallas internas en láminas de material compuesto con mayores probabilidades de éxito. A la par con el CTDF, se concibió un criterio de estimación de la profundidad de estos defectos que, aunque brinda buenos resultados para aquellos superficiales y más contrastados térmicamente, pierde calidad en la estimación de la profundidad de defectos más profundos y más débiles en su contraste térmico. Considerando este problema, en este artículo se adelanta una revisión de dicho criterio con el ánimo de definir un método más robusto para el cálculo de pro-fundidad de los defectos contrastados por la técnica CTDF. Los resultados de la ejecución de este nuevo algoritmo sobre imágenes sintéticas generadas a partir de una lámina artificial de CFRP (mediante el software ThermoCalc6L) muestran un mejor desempeño en la estimación, reduciendo el error relativo promedio a más de la mitad.

After the detection of internal defects in materials, the characterization of these plays a decisive role in order to establish the severity of these flaws. Finite difference thermal contrast (FDTC) is a new technique proposed recently for contrast enhancement in sequences of thermal images in order to allow the detection of internal flaws in composite slabs with greater probability of success. Besides FDTC, a criterion was also conceived for the estimation of the depth of the detected defects, which brings good results for shallow and strong contrast defects, but poor estimations for deeper and weaker defects. Considering this problem, a revision of the original criterion is carried out in this paper to define a new and robust criterion for estimating the depth of defects, applied after FDTC en-hancement and flaws detection. Results of the execution of the revised algorithm on a synthetized thermal sequence from an artificial CFRP slab (using ThermoCalc6L software) show a better performance of the estimation task, reducing the average relative error by more than half., Posterior a la detección de defectos internos en los materiales, su caracterización juega un papel decisivo para establecer la seve-ridad de dichas fallas. El contraste térmico por diferencias finitas (CTDF) es una técnica novedosa propuesta recientemente para el mejoramiento del contraste en secuencias de imágenes térmicas que permite la detección de fallas internas en láminas de material compuesto con mayores probabilidades de éxito. A la par con el CTDF, se concibió un criterio de estimación de la profundidad de estos defectos que, aunque brinda buenos resultados para aquellos superficiales y más contrastados térmicamente, pierde calidad en la estimación de la profundidad de defectos más profundos y más débiles en su contraste térmico. Considerando este problema, en este artículo se adelanta una revisión de dicho criterio con el ánimo de definir un método más robusto para el cálculo de pro-fundidad de los defectos contrastados por la técnica CTDF. Los resultados de la ejecución de este nuevo algoritmo sobre imágenes sintéticas generadas a partir de una lámina artificial de CFRP (mediante el software ThermoCalc6L) muestran un mejor desempeño en la estimación, reduciendo el error relativo promedio a más de la mitad.

After the detection of internal defects in materials, the characterization of these plays a decisive role in order to establish the severity of these flaws. Finite difference thermal contrast (FDTC) is a new technique proposed recently for contrast enhancement in sequences of thermal images in order to allow the detection of internal flaws in composite slabs with greater probability of success. Besides FDTC, a criterion was also conceived for the estimation of the depth of the detected defects, which brings good results for shallow and strong contrast defects, but poor estimations for deeper and weaker defects. Considering this problem, a revision of the original criterion is carried out in this paper to define a new and robust criterion for estimating the depth of defects, applied after FDTC en-hancement and flaws detection. Results of the execution of the revised algorithm on a synthetized thermal sequence from an artificial CFRP slab (using ThermoCalc6L software) show a better performance of the estimation task, reducing the average relative error by more than half., Posterior a la detección de defectos internos en los materiales, su caracterización juega un papel decisivo para establecer la seve-ridad de dichas fallas. El contraste térmico por diferencias finitas (CTDF) es una técnica novedosa propuesta recientemente para el mejoramiento del contraste en secuencias de imágenes térmicas que permite la detección de fallas internas en láminas de material compuesto con mayores probabilidades de éxito. A la par con el CTDF, se concibió un criterio de estimación de la profundidad de estos defectos que, aunque brinda buenos resultados para aquellos superficiales y más contrastados térmicamente, pierde calidad en la estimación de la profundidad de defectos más profundos y más débiles en su contraste térmico. Considerando este problema, en este artículo se adelanta una revisión de dicho criterio con el ánimo de definir un método más robusto para el cálculo de pro-fundidad de los defectos contrastados por la técnica CTDF. Los resultados de la ejecución de este nuevo algoritmo sobre imágenes sintéticas generadas a partir de una lámina artificial de CFRP (mediante el software ThermoCalc6L) muestran un mejor desempeño en la estimación, reduciendo el error relativo promedio a más de la mitad.

En el presente artículo se presenta el desarrollo de un nuevo modelo matemático basado en la discretización por diferencias finitas del modelo de Fourier de propagación del calor en 3D, a partir del cual se propone una nueva técnica para detectar defectos en láminas de material compuesto, inspeccionadas mediante un experimento de Termografía Activa Pulsada (TP). El modelo discreto generado es fácilmente adaptable a la estructura de un filtro espacial, que al aplicarlo a la secuencia de imágenes infrarrojas adquiridas en tal experimento, permite contrastar adecuadamente las posibles zonas defectuosas internas con respecto a las zonas sanas del material. Para evaluar el desempeño de la técnica propuesta, se utilizan secuencias térmicas artificiales a través del programa ThermoCalc6L, con el cual es posible calcular las distribuciones dinámicas de la temperatura en capas sólidas anisotrópicas, simulando defectos internos y diferentes esquemas de excitación térmica. Los resultados muestran que en una secuencia de imágenes con información de temperatura, el método propuesto ofrece una mejor diferenciación entre los defectos y la información térmica de fondo con respecto a otros métodos comunes como el Contraste Absoluto Diferencial (CAD), y se ejecuta más rápidamente que el método clásico de filtrado térmico en 3D., In this paper, a new mathematical model based on finite difference discretization of the Fourier’s 3D heat propagation model, is shown. From this one, a new technique is proposed to detect flaws in composite thin layers, under evaluation by a Pulsed Active Thermography experiment. The discrete model defined is easily adaptable to a spatial filter structure, which can be applied to the infrared sequence of images acquired from that thermography experiment, to obtain a better contrast between possible internal flaws and sound regions of material. The performance of the technique proposed is evaluated using artificial thermal sequences generated by ThermoCalc6L, software that is able to compute dynamic thermal distributions in anisotropic layered solids, simulating defects and different excitation sets. Results show that this technique offers a better contrast between defect and background thermal information, than other common technique like Differential Absolute Contrast (DAC), and it runs faster than the classic 3D thermal filtering method.

In this paper, a new mathematical model based on finite difference discretization of the Fourier’s 3D heat propagation model, is shown. From this one, a new technique is proposed to detect flaws in composite thin layers, under evaluation by a Pulsed Active Thermography experiment. The discrete model defined is easily adaptable to a spatial filter structure, which can be applied to the infrared sequence of images acquired from that thermography experiment, to obtain a better contrast between possible internal flaws and sound regions of material. The performance of the technique proposed is evaluated using artificial thermal sequences generated by ThermoCalc6L, software that is able to compute dynamic thermal distributions in anisotropic layered solids, simulating defects and different excitation sets. Results show that this technique offers a better contrast between defect and background thermal information, than other common technique like Differential Absolute Contrast (DAC), and it runs faster than the classic 3D thermal filtering method., En el presente artículo se presenta el desarrollo de un nuevo modelo matemático basado en la discretización por diferencias finitas del modelo de Fourier de propagación del calor en 3D, a partir del cual se propone una nueva técnica para detectar defectos en láminas de material compuesto, inspeccionadas mediante un experimento de Termografía Activa Pulsada (TP). El modelo discreto generado es fácilmente adaptable a la estructura de un filtro espacial, que al aplicarlo a la secuencia de imágenes infrarrojas adquiridas en tal experimento, permite contrastar adecuadamente las posibles zonas defectuosas internas con respecto a las zonas sanas del material. Para evaluar el desempeño de la técnica propuesta, se utilizan secuencias térmicas artificiales a través del programa ThermoCalc6L, con el cual es posible calcular las distribuciones dinámicas de la temperatura en capas sólidas anisotrópicas, simulando defectos internos y diferentes esquemas de excitación térmica. Los resultados muestran que en una secuencia de imágenes con información de temperatura, el método propuesto ofrece una mejor diferenciación entre los defectos y la información térmica de fondo con respecto a otros métodos comunes como el Contraste Absoluto Diferencial (CAD), y se ejecuta más rápidamente que el método clásico de filtrado térmico en 3D.

The durability of an exterior finish is affected by the characteristics of the wood. Satisfactory finish life is usually more difficult to achieve on woods of higher density. All wood shrinks as it loses moisture and swells as it absorbs moisture, but some species are more stable than others. Species that shrink and swell the most cause more stress on paint films than woods that are more stable [1]. To this end, let us recall that a painting on wood can be considered as a layered structure: The wood support is coated with a number of superposed priming layers made from mixtures of gesso and glue. A frequent fault resulting from such a system is the formation of detached regions inside the layered structure caused by the shrinkage process of the wood support [2]. Obviously, wood deteriorates more rapidly in warm, humid regions with respect to cool or dry places [3]. The influence of wood conditions on surface coatings is a critical point that should be monitored and that depends on environmental parameters such as microclimate. To prevent and control the effects, keeping costs down, a non-destructive monitoring of wood support behavior under thermal stress is needed. In this work, an integrated approach based on traditional and innovative (HI, PT and NIR) techniques was conducted on a primed support of poplar wood with a complex-shape surface containing areas of artificial defects at several depths due to the influence of the support on the various layers. The obtained results could be arranged, if integrated into a multidisciplinary approach, in order to define and design the conservation of the wooden artifacts.