# Dynamic Line Scan Thermography Parameter Design via Gaussian Process Emulation
## 1 Introduction  
Active thermography is widely recognized as a fast, reliable and contactless non-destructive inspection technique [1]. It can be performed in a stationary manner in which the sample to be inspected remains at the same location. This way, the object is easily heated using a heating source, and the cooling of the sample is registered using a thermal camera. This method limits the size of the object since the sample has to fit in the field of view of the camera. It is possible to examine larger samples by placing the thermal camera at a greater distance from the sample. The downside of placing the camera further away from the sample is the resolution reduction in a specified region. In order to detect a defect with sufficient certainty, the defect has to have an area of at least 3 \(\times\) 3 pixels [2]. Larger samples can be inspected using dynamic line scan thermography (DLST). This technique uses a heat source and a thermal camera in tandem, which moves relative to the sample to be inspected. This can be achieved in two ways: either the camera and heating source are moved above the object using a robotic arm, or the specimen can translate on a conveyor belt underneath the heating source and the camera [3, 4]. Since dynamic line scan thermography is a relatively new technique, it is less widely spread in comparison to other nondestructive testing methods. An expert skilled in the art has to define the DLST measurement parameters in order to prevent time-intensive trial and error attempts to find a workable parameter set. In this work, we focus on the movement velocity, the distance between the heat source and thecamera, the heating power, the start depth of the defect, the diameter of the defect, the height of the camera and the ambient temperature. Several studies have been performed in order to simplify the search for these DLST measurement parameters. Finite element simulations have been used in order to update the parameters according to measurements [5]. Response surfaces are used as approximation in order to find the best parameters based on the characteristics of the defect (depth, dimension) and the thermal properties of the material [2]. Using the response surface and some fixed parameters provided by the inspector of a specimen, the best matching set of parameters is predicted. A response surface can be generated using data from multiple measurements. However, in order to create such a response surface, a large amount of measurements are needed. Generally, this is a time-consuming and costly endeavor. Therefore, a response surface is often built from data gathered in multiple finite element simulations. The amount of simulations matches the amount of needed measurements; nonetheless, performing simulations is cheaper, cost wise and time wise. The simulation performed for this manuscript consists of a flat bottom hole plate heated by a line heater moving above the sample. The simulated object is a flat bottom hole plate since this is widely used in scientific research on thermography. The thermal behavior of flat bottom holes best resembles the response expected by most defects, whereby active thermography is used as an inspection method. Such defects are delaminations, lateral cracks, areas of porosity, etc. Attempts are made to create a standard for thermal imaging based on the use of flat bottom hole plates [6; 7]. Therefore, this research is limited to flat bottom hole plates. A different approach to predict an optimal parameter set is to use artificial intelligence. For instance, it is possible to train a reinforcement agent to search for the best parameters to detect multiple defects in a flat bottom hole plate. However, training the reinforcement learning algorithm requires more simulations than generating the response surface and, therefore, is less interesting. Computer simulations are used in a wide range of scientific and engineering challenges [8].