## 1 Introduction  
Active thermography is recognised worldwide as a promising technique to perform non-destructive inspections [1]. The technology found its place in diverse domains compared to before when the technique was mainly used in the aerospace industry. The main drawback of performing active thermography measurements on large samples was the fact the thermal camera or the structure had to moved in order to inspect the whole object. Moving the camera or object in multiple steps is highly sensitive to mistakes and therefore a solution was developed by NASA. The technique was based on the ideas of [PERSON] [2] and [PERSON] [3] of moving the camera and heat source in tandem. When combining the heat source and the camera in a solid set-up, the heating direction, homogeneity, distance, etc. are the same for every frame. Constraining the heat source and the camera together enables optimisation of the measurement set-up in an accurate way. In Figure 1, a visualisation of a Dynamic Line Scan Thermography set-up with the most important parameters is shown.  
Several studies have been performed in order to optimise a Dynamic Line Scan Thermography set-up. Most of this optimisation research focuses on a specific use case or a specific material group [4; 5; 6]. Dynamic Line Scan Thermography (DLST) is often linked with carbon fibre-reinforced plastic parts used in the aerospace industry. The technique, however, is also used for different cases such as paper, wood, concrete, etc. [7]. Optimising the measurement setup in order to inspect a certain sample results in showing that it is possible to use DLST. [PERSON] et al. [6] used finite element updating in order to find the best parameter set for the sample to be inspected. The aim of this research is to provide a general optimisation routine based on response surfaces. Instead of performing an optimisation for a specific use case, the correlation between the set-up variables and the measured contrast is investigated. Response surfaces are widely used in order to find an optimum while reducing the amount of computationally intensive simulations [8; 9; 10]. Performing optimisations using response surfaces is used in a variety of applications [11; 12; 13; 14], and the use of response surfaces is still being optimised [15]. As can be seen in Figure 1, there are many parameters that influence the optimal measurement set-up for DLST. Some of these parameters can be hard to assess such as the defect diameter and the starting depth of the defect. During production, a person involved in the process can estimate the common properties of defects inside the object. The measurement set-up could be optimised for these estimated properties using a computationally expensive optimisation. If the estimations were faulty, an additional optimisation should be performed, resulting in supplementary simulations. Each variation in a parameter results in the necessity to perform a new optimisation, and therefore the use of response surfaces is justifiable. This paper discusses the creation of the response surface used for the optimisation and offers an explanation for the influence of every variable used. How the response surface is employed in this research is described in the second part of this paper. Performing thermal inspections based on the optimised parameters is explained in part three.