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This study presents photothermal imaging results of subsurface material defects within fiber metal laminates utilizing the virtual wave concept. Therefore, we theoretically analyze the propagation of the virtual wave signal in a hybrid composite laminate via the method of images. For provoking local material damage, the hybrid composite sample is subjected to a defined impact loading. The results obtained from photothermal defect imaging, utilizing rectangular laser pulse excitation, are compared with results obtained from 3D x-ray computed tomography. To sum up, we demonstrate a fast, non-invasive, and easily interpretable reconstruction of defects within macroscopic hybrid composite laminates based on the virtual wave concept. [ABSTRACT FROM AUTHOR]

In this Tutorial, we aim to directly recreate some of our "aha" moments when exploring the impact of heat diffusion on the spatial resolution limit of photothermal imaging. Our objective is also to communicate how this physical limit can nevertheless be overcome and include some concrete technological applications. Describing diffusion as a random walk, one insight is that such a stochastic process involves not only a Gaussian spread of the mean values in space, with the variance proportional to the diffusion time, but also temporal and spatial fluctuations around these mean values. All these fluctuations strongly influence the image reconstruction immediately after the short heating pulse. The Gaussian spread of the mean values in space increases the entropy, while the fluctuations lead to a loss of information that blurs the reconstruction of the initial temperature distribution and can be described mathematically by a spatial convolution with a Gaussian thermal point-spread-function. The information loss turns out to be equal to the mean entropy increase and limits the spatial resolution proportional to the depth of imaged subsurface structures. This principal resolution limit can only be overcome by including additional information such as sparsity or positivity. Prior information can be also included by using a deep neural network with a finite degrees of freedom and trained on a specific class of image examples for image reconstruction. [ABSTRACT FROM AUTHOR]

In this paper, we present a 3D photothermal imaging tool to detect subsurface defects in anisotropic media using the virtual wave concept. In addition, we propose a novel approach to compute the temperature contrast using a virtual wave signal, which enables a temporal noise-free representation of the contrast temperature signal. The results obtained with the proposed imaging tool are compared with those obtained using computed tomography for a carbon fiber-reinforced polymer sample containing a delamination caused by a defined impact. To sum up, this work presents a fast, easily interpretable, and efficient 3D photothermal defect reconstruction and visualization tool. [ABSTRACT FROM AUTHOR]

In this Tutorial, we combine the different scientific fields of information theory, thermodynamics, regularization theory, and non-destructive imaging, especially for photoacoustic and photothermal imaging. The goal is to get a better understanding of how information gaining for subsurface imaging works and how the spatial resolution limit can be overcome by using additional information. Here, the resolution limit in photoacoustic and photothermal imaging is derived from the irreversibility of attenuation of the pressure wave and of heat diffusion during the propagation of the signals from the imaged subsurface structures to the sample surface, respectively. The acoustic or temperature signals are converted into so-called virtual waves, which are their reversible counterparts and which can be used for image reconstruction by well-known ultrasound reconstruction methods. The conversion into virtual waves is an ill-posed inverse problem, which needs regularization. The reason for that is the information loss during signal propagation to the sample surface, which turns out to be equal to the entropy production. As the entropy production from acoustic attenuation is usually small compared to the entropy production from heat diffusion, the spatial resolution in acoustic imaging is higher than in thermal imaging. Therefore, it is especially necessary to overcome this resolution limit for thermographic imaging by using additional information. Incorporating sparsity and non-negativity in iterative regularization methods gives a significant resolution enhancement, which was experimentally demonstrated by one-dimensional imaging of thin layers with varying depth or by three-dimensional imaging, either from a single detection plane or from three perpendicular detection planes on the surface of a sample cube. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new parameter estimation and image reconstruction approach for the photothermal testing of composite materials. Therefore, the full multidimensional evaluation method, virtual wave concept, is extended to estimate the orthotropic thermal diffusivity tensor and to reconstruct the initial temperature distribution after a laser spot heating in an orthotropic material. We establish a formal relationship between the virtual speed of sound tensor and the thermal diffusivity tensor. Furthermore, we show how prior information in the form of positivity and sparsity can be incorporated in the regularization process to improve the solution of the inverse imaging problem. In a second step, the initial temperature distribution is reconstructed by applying ultrasonic imaging methods on the calculated 3D bimodal virtual wave field. This new approach is validated on simulation and experimental data of a unidirectional carbon fiber reinforced polymer. The information loss that results from entropy production during heat diffusion can be partly compensated by including prior information. This allows an accurate parameter estimation and a high-resolution image reconstruction. [ABSTRACT FROM AUTHOR]