In a wide range of disciplines, such as aeronautical, automotive, and structural engineering, infrared thermography (IRT) has demonstrated promising performance for inspecting and monitoring structures. This chapter provides a survey of IRT as a nondestructive assessment method for composite materials subjected to impact loads that cause critical damage scenarios inside the structure. The chapter starts with a general overview of the principles of IRT and its historical evolution. Various methodologies of thermography inspection, such as optically, mechanically, and inductively stimulated thermography, are described for the evaluation of impact damage in composite structures. In the heating waveforms and data processing section, the relevant processing techniques and data analysis algorithms for each type of the above-mentioned thermography methodologies based on the applied waveform are presented. Case studies of composite specimens suffering from impact damage are provided at the end of the chapter to demonstrate the applicability and performance of IRT inspection.@en

The infrared thermography (IRT) technique has shown great potential with many applications in a variety of fields, such as aerospace, petroleum, civil engineering, and so forth. This chapter is designed to provide an overview of the method of IRT in non-destructive material evaluation. It begins with the general introduction and development of the IRT. It follows the basic principles of the electromagnetic spectrum and radiative energy concepts. Following the introduction of different types of thermography, applications in the non-destructive testing fields are provided. The next section focuses on recent trends and developments, in particular the integration of IRT with other technologies namely ultrasonic, acoustic emission, and so on. By assigning a data analysis section, data processing techniques are described by dividing them into two conventional and advanced categories. The numerical modeling and simulation of the IRT inspection process are then presented as beneficial applications. In addition, the relevant standards have been specified in a short separate section. To testify to the effectiveness of the IRT, a case study of a steel sample inspection is provided at the end of the chapter itself.

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Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of artworks while avoiding the loss of any precious materials that make them up. The use of Infrared Thermography is an interesting concept since surface and subsurface faults can be discovered by utilizing the 3D diffusion inside the object caused by external heat. The primary goal of this research is to detect defects in artworks, which is one of the most important tasks in the restoration of mural paintings. To this end, machine learning and deep learning techniques are effective tools that should be employed properly in accordance with the experiment’s nature and the collected data. Considering both the temporal and spatial perspectives of step-heating thermography, a spatiotemporal deep neural network is developed for defect identification in a mock-up reproducing an artwork. The results are then compared with those of other conventional algorithms, demonstrating that the proposed approach outperforms the others.@en

Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of the artworks while avoiding the loss of any precious materials that make it up. The use of Infrared Thermography (IRT) is an interesting concept since surface and subsurface faults can be discovered by utilizing the 3D diffusion inside the object caused by external heat. The primary goal of this research is to detect defects in artworks, which is one of the most important tasks in the restoration of mural paintings. To this end, a spatiotemporal deep neural network (STDNN) is utilized for defect identification in a mock-up reproducing an artwork, taking into account both the temporal and spatial perspectives of step-heating (SH) thermography. Finally, the outcomes are compared to those of other conventional algorithms.@en

Inspection of cultural heritage objects plays nowadays paramount importance around the world. Non-destructive inspection is a must and a necessity in order to preserve the integrity of the artwork without losing any precious material composing it. The use of thermal non-destructive inspection is a good idea since, by exploiting the 3D diffusion inside the object, triggered by external radiation, surface and subsurface defects may be revealed. To do this, the long-wave infrared (LWIR) spectrum is usually exploited in combination with a thermal camera. In the cultural heritage field, the main problem to solve to detect as much as possible thermal imprints linked to invisible defects is the minimisation of the impact of emissivity variations caused by pigments composing the colours. A simple, effective, solid, and optimized method for decorated paintings is here applied right after some preliminary results. It is based on active thermography as the modality of inspection, and a thermal stimulus provoked by halogen lamps; thermal images recorded on a panel painting including man-made defects have been analysed and processed in MATLAB® environment. After extraction of de-nosing functions based on the heating and cooling steps, two methods are proposed to enhance the de-noised thermograms. Then, popular methods of Pulsed Phase Thermography (PPT) and the Principal Component Thermography (PCT) are applied. In the end, after minimizing the effect of emissivity variation, an optimization fusion proposal through post-processing the emissivity adjusted thermograms is provided. A brief but exhaustive review introduce and guide the readers towards the problem for which the authors took a step ahead via a proposal based on image fusion.

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