# Automatic Evaluation of the Robotic Production Process for an Aircraft Jet Engine Casing
## 1 Introduction
The mentioned disadvantages were eliminated using an industrial robot and automatic welding. The quality of robotic TIG-welded joints depends on the correction of the electrode torch position and stabilization of arc-welding parameters, such as current, voltage, and shielding gas flow rate. Corrections of the robot TCP (tool center point) position, related to the TIG welding path in this case, could be implemented in various ways: by applying video systems [5,6] with a single camera [7], a camera with a laser pointer [8,9], or a 3D measurement system [10]. A dual-camera correction system for laser welding is discussed in [11]. In very complex cases, robot trajectory adaptation requires the use of second robot with a laser triangulation sensor [12]. In the area of control algorithms, there are developed solutions with elements of machine intelligence: fuzzy logic [13, 14, 15], artificial neural networks [14, 15, 16, 17], or genetic algorithms [18]. In the first mentioned solution, using a knowledge base developed from the experience of human welders, a fuzzy logic algorithm was proposed to track the welding pool. For artificial neural networks, there has been a very defined increase lately in the applications of deep learning solutions [19]. An alternative method for welded workpiece displacement correction are reference system adaptation algorithms--this approach was presented in [20] and concerned the welding of tubular workpieces for jet engines. To solve the problem of improving the quality of welds, not only were complex neuron algorithms used, but also elements of virtual reality (VR) [21], where the welding process was optimized using its digital 'twin' [16]. VR was also used for programming welding robot trajectories, using a digital description of the movements made by an experienced welder [22]. The welding process quality does not only rely on the location of the TCP, but also on the welding parameters. In [23], the effect of various parameters on the welding process was investigated. The investigation focused on the depth of melting, the microstructure, and the measured hardness of welds produced in mild steel with robotic TIG welding. The variables qualified for that investigation included the arc voltage, the welding current, and the welding speed. In [24] an algorithm, which used an element to predict the weld seam width, based on fuzzy logic and deep learning was applied for the following parameters: welding current, arc length, and welding speed. Another paper that focused on the optimization of TIG welding by achieving a conforming shape of weld seams is [25]. The process input parameters qualified in the paper were welding current, arc length, and electrode torch speed; the output variables investigated were melting depth, depth to width ratio, heat input, and heat affected zone width. This paper investigated the use of statistical methods to determine significant indicators determining the quality of the performance of the robotic process based on real production data. It was hard to find similar work, mainly because process parameter data and production are confidential company data, which are rarely shared due to their impact on the economic indicators, especially in aviation industry. A small number of references focused on detailed analysis and forecasting of a weld' s quality based on the welding process parameters are now complemented by the problem considered in this work.