## 3 Experimental Setup and Plan  
The WAAM method generates parts by depositing weld beads onto each other. This technique produces weld beads utilizing the GMAW process. The research employed GMAW equipment, including the Migatronic Mig 501 power supply and PARS FEED 4520C wire feeding system. A cooling system (Figure 4C), PARS COOL, and a gas preheater, the Neptune, were also employed. In this research, a constant voltage Joosha welding instrument was used. In this instrument, the voltage can be adjusted and the amperage will change during the welding process. To facilitate the execution of experimental tests during construction, a three-axis controlled table was utilized, adapted from an FP4M milling machine table for movement in three axes at adjustable speeds. Initial experiments were conducted to investigate weld bead geometry on a weld line and determine the optimal geometry for manufacturing thin-walled parts. The nozzle of the welding machine was connected to the milling table for initial assessments and obtaining desirable parameters for creating welds with suitable geometry, as depicted in Figure 7, where GMAW is shown with milling table motion control. Figure 6: The image of vertical, horizontal, and diagonal borders and its decomposition by two-dimensional wavelet transform [34]. Figure 7: Gas metal arc welding system with milling table motion control. In additive manufacturing, parts are produced layer by layer, each serving as the substrate for the next layer if parameters ensuring a uniform welding surface are not utilized. Ultimately, the produced component may exhibit defects. Metal parts protected with gas-shielded electric arc welding were employed to obtain an initial image for weld geometry determination. The metal surface was captured using a digital camera. Subsequently, the digital camera lens, specifically the DFK-23 GM021, was calibrated according to the specifications in Table 1.  
Upon entering the computer, the captured image was converted into a grayscale image. Then, brightness and contrast were adjusted to enhance the image's clarity. The selection of appropriate values for the two image attributes, brightness and contrast, depended on the lighting conditions and image acquisition. The following steps describe the image processing using MATLAB R2024b software for calculating the upper weld edge. **Step One:** Initially, the image underwent morphological expansion using the 'imopen' function. Morphological expansion of the image allowed for the selective removal of small components from the image, ensuring that no spurious elements interfere with the primary features of the image. **Step Two:** In this stage, image contrast was enhanced using MATLAB software's 'imadjust' command to reduce image blur. Additionally, a 2D filter was predefined using the 'fspecial' function, and an unsharp mask, as per Equation (10), was applied to enhance image clarity. \[\frac{1}{(\alpha+1)}[-\alpha\;\alpha-1-\alpha\;\alpha-1\;\alpha+5\;\alpha-1- \alpha\;\alpha-1-\alpha\;] \tag{10}\]  
**Step Three:** In this stage, the image's edges were sharpened radially, resulting in a more distinct contrast between the weld edges and the image background, facilitating the extraction of weld edges. The resulting edge-sharpened image was then converted into a binary image. Subsequently, the binary image underwent morphological erosion, followed by linear dilation at a 45-degree angle using the 'imdilate' function to connect regions within the image seamlessly. **Step Four:** In this stage, any holes within objects in the binary image were filled using MATLAB software's 'imfill' command. This step was carried out to make the welded regions more distinct and eliminate scattered small components around the image, with components containing fewer than 1000 pixels being removed. **Step Five:** A set of connected points was labeled using MATLAB software's 'bwlabel' function during this stage. Noise and unrelated points to the weld, such as markings on the workpiece, were removed.