## 3 Case Study: Experimental Verification and Comparison Results  
### Experimental Setup Description  
A case study was completed to verify the effectiveness of the proposed method for the fault diagnosis task and its applicability to actual industrial fields. Spot welding is one of the joining methods broadly used in various manufacturing processes and is performed in automotive body manufacturing processes. Welding faults are directly related to product quality degradation. However, it is difficult to identify a defect in spot welding without damaging the specimen. Several nondestructive tests exist, but it is impossible to do this for all products. Therefore, as an alternative, we performed a defect diagnosis of welding quality using only the accelerometer. The spot-welding process using the industrial robot imitated in the case study is straightforward to automate. Therefore, transfer learning can be particularly effective for diagnosing failures of different welding products in automated welding processes. A testbed that mimics the spot-welding process using an industrial six-axis articulated robot used in the actual automotive body manufacturing process was installed. Machinery, including manufacturing robots, was investigated, and similar components were selected as much as possible. The HS-180 manufactured by Hyundai Robotics [42] was used as the manufacturing robot. The robot's payload is up to 180 kg, the maximum height is 2088 mm, and the maximum length is 3128 mm. A spot-welding servo gun made by Obara [43]  
Figure 3: Flowchart of the model transfer and the fine-tuning procedure. was used for spot welding. An experimental jig and other facilities were also designed as in the field. As depicted in Figure 4, the accelerometer PCB 353B03 model was installed perpendicular to the electrode at the bottom of the spot-welding gun frame. Vibration signals were collected when welding was repeatedly performed. The experiment was conducted under a sampling frequency of 12.8 kHz and a sampling time of 35 s using the NI9232 DAQ. In this case study, the occurrence of a small nugget was selected as the major failure state to observe. If the welding nugget is not stably formed, the joint does not proceed correctly. This is a typical welding quality fault, which causes a defect in the finished product. In terms of process variables, such a failure occurs when the weld current or the weld time is low or when the electrode pressing force is excessive compared to the weld current. In addition, small nugget often occurs when the dimension of the electrode tip is large due to wear. It can be caused by problems with various detailed components of the industrial robot, such as reducers, input gears, and motors. A cross-section test-based quality inspection was performed on the welded specimen to identify the failure. Cross-section test images are illustrated in Figure 5. Data were collected in three cases according to the type of welding specimen: mild steel, galvanized (GI) steel, and galvan annealed (GA) steel. Primarily, the mechanical properties are different because the specimen materials are different for each welding specimen. The three types of specimens used in the case study have a common size of 300 mm in width, 100 mm in length, and 0.8 mm in thickness. The chemical composition of the mild steel specimen is C: 0.018%, Si: 0.002%, Mn: 0.201%, P: 0.009%, and S: 0.005%. The zinc coating mass of the GI steel specimen is 18.3 g/mm\({}^{2}\) and 17.8 g/mm\({}^{2}\), and the chemical composition is C: 0.02%, P: 0.013%, S: 0.011%, Mn: 0.13%, and Sol-Al: 0.03%. The GI steel specimen is zinc alloy steel with a zinc mass of 120 g/mm\({}^{2}\), and its chemical composition is C: 0.25%, P: 0.10%, S: 0.04%, and Mn: 1.35%. Therefore, the welding conditions, such as the electrode pressing force and weld current, were also set differently according to the properties of each specimen material. Table 2 lists the welding conditions for each specimen material in detail.