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The detection and characterization of surface cracks in steel specimens prior to damage is a technologically and economically highly significant task and is of utmost importance when it comes to safety-relevant structures. In steel production where steel billets at high temperatures have to be inspected while moving a number of well-established NDT methods cannot be applied. Laser thermography however is a promising candidate to serve as a fast, non-contact and remote tool in this case. We present a study that shows that the crack detection capabilities of laser thermography can be extended also to specimens at high temperature. A combination of inductive and laser heating allows to systematically study the contrast formation as well as the optimization of the important measurement parameters. The experiments are accompanied by FEM simulations that provide a better insight of the physical correlations and support the experimental developments. The aim of these studies is to develop a system with high inspection speed and detection performance to be in-line operated under the hostile environment of steel production lines.
The detection and characterization of surface breaking cracks in steel prior to damage is a technologically as well as economically important task especially for safety-relevant structures. Detection of small cracks already during the steel production process might significantly reduce the risk of failure and reduce production costs due to an obsolete post-processing.
However, the hostile environmental conditions (high temperature specimens) together with very strict requirements in current steel production (production speeds, in-line testing and evaluation) are challenging and render many well-established NDE techniques hardly applicable.
We present an approach to use laser thermographic testing as a fast, remote and contactless NDE method, that addresses these challenges and might ultimately allow for online crack detection.
The basic idea of laser thermographic testing, introduced by Kubiak in 1968, is the monitoring of the heat flow as induced by local heating. Disturbances within the heat flow generated by the presence of surface cracks can then be analyzed by image processing algorithms, as we have shown in previous work.
The aim of the presented work is to advance laser thermographic testing to be applicable to the specific conditions of steel production environments. This purpose was met by the development of a laboratory setup that allows us to simulate production conditions, as rolling speed, specimen temperature, laser heating power and study their influence on crack detection performance. This parametric study enabled us to develop and improve data processing and crack detection algorithms with the final goal of providing optimized in-line crack detection. The studies were accompanied by comprehensive FEM simulations to intensify the understanding of the contrast formation as well as the crucial parameters influencing the performance of the method.
Thermal waves are solutions of the heat diffusion equation for periodic boundary conditions and can be seen analogously to strongly damped waves. Although the underlying differential equation differs from the wave equation, the essential property for analogy between both equations is linearity such that superposition applies. This linearity is maintained even after a linear transformation, such as the Fourier transform from time to frequency domain. It follows that the temporal superposition principle is already used in active thermography, e.g. in pulsed thermography, as a superposition of many individual frequencies. However, the systematic spatial superposition has not yet been fully exploited, mainly due to a lack of suitable energy sources.
As a first step, we are investigating how thermal wave fields of arbitrary space-time structures can be engineered using structured laser illumination. The proof of principle was shown using a laser coupled projector. Unfortunately, the available optical output power was limited due to the thermal stress limit of the device. That is why we are working towards a more sophisticated moving 1D array of high-power diode lasers. We characterized the novel light source and believe that apart from the benefit of spatial and temporal illumination it can combine the temporal regimes of impulse and lock-in thermography.
In a second step, we investigate moving and oscillating line sources with different line shapes. We use a Green’s Function ansatz to analytically model the thermal wave propagation of structured 1D laser illumination in isotropic materials. Furthermore, we show some methods how they can be implemented. With this technique, we were able to accelerate our detection method firstly presented in for vertical narrow defects by factor three. Generally, we believe that this technique opens up similar opportunities than in other NDE methods. High-resolution ultrasound, for example, is also based on the superposition of single emitters and a recent concept suggests an option to deal with the diffusion wave character of the thermal waves.
The separation of two closely spaced defects in fields of Thermographic NDE is very challenging. The diffusive nature of thermal waves leads to a fundamental limitation in spatial resolution. Therefore, super resolution image reconstruction can be used. A new concerted ansatz based on spatially structured heating and joint sparsity of the signal ensemble allows for an improved reconstruction of closely spaced defects. This new technique has been studied using a 1D laser array with randomly chosen illumination pattern.
This paper presents the results after applying super resolution algorithms, such as the iterative joint sparsity (IJOSP) algorithm, to our processed measurement data. Different data processing techniques before applying the IJOSP algorithm as well as the influence of regularization parameters in the data processing techniques are discussed. Moreover, the degradation of super resolution reconstruction goodness by the choice of experimental parameters such as laser line width or number of measurements is shown.
The application of the super resolution results in a spatial resolution enhancement of approximately a factor of four which leads to a better separation of two closely spaced defects.
Thermographic nondestructive evaluation (NDE) is based on the interaction of thermal waves with inhomogeneities. These inhomogeneities are related to sample geometry or material composition. Although thermography is suitable for a wide range of inhomogeneities and materials, the fundamental limitation is the diffusive nature of thermal waves and the need to measure their effect radiometrically at the sample surface only. The propagation of the thermal waves from the heat source to the inhomogeneity and to the detection surface results in a degradation in the spatial resolution of the technique. A new concerted ansatz based on a spatially structured heating and a joint sparsity of the signal ensemble allows an improved reconstruction of inhomogeneities. As a first step to establish an improved thermographic NDE method, an experimental setup was built based on structured 1D illumination using a flash lamp behind a mechanical aperture. As a follow-up to this approach, we now use direct structured illumination using a 1D laser array. The individual emitter cells are driven by a pseudo-random binary pattern and are additionally shifted by fractions of the cell period. The repeated measurement of these different configurations enables to illuminate each spot of the sample surface in lateral direction. This allows for a reconstruction that makes use of joint sparsity.
The measured data set is processed using super resolution image reconstruction algorithms such as the iterative joint sparsity (IJOSP) algorithm. Using this reconstruction technique and 150 different illumination patterns results in a spatial resolution enhancement of approximately a factor of four compared to the resolution of 5.9 mm for homogenously illuminated thermographic reconstruction.
Further, new data processing techniques have been studied before applying the IJOSP algorithm that are more performant or less prone to errors regarding image reconstruction. The choice of regularization parameters in data processing as well as experimental parameters such as the illumination pattern as a variable heat flux density (i.e., the Neumann boundary condition for convolution with the constant Green's function) have a big influence on the reconstruction goodness. With analytical-numerical modelling and numerical FEM simulations, we studied the influence of the experimental parameters on the result of the non-linear IJOSP reconstruction. This has also been investigated experimentally e.g. using different laser line widths or more measurements per position. These studies are used to derive optimal conditions for a certain measurement image reconstruction technique.
The work to be presented focuses on our most recent studies to laser excited super resolution (SR) thermography. The goal of nondestructive testing with SR is to facilitate the separation of closely spaced defects. Photothermal SR can be realized by performing structured illumination measurements in com-bination with the use of deconvolution algorithms in post-processing. We explain that stepwise as well as continuous scanning techniques are applicable to generate structured illumination measurements. Finally, we discuss the effect of experimental parameters and image processing techniques to find the optimal SR technique which leads to the highest reconstruction quality within laser thermography.