## 6 Transformer Disassembly Results  
### Experimental Results of Fuel Tank Cover Disassembly  
We are establishing a new intelligent disassembly factory for electrical equipment, and here we provide a photo of the transformer disassembly area, as shown in Figure 14.  
The experiments utilized an RGB-D camera for data acquisition, employing multimodal fusion to integrate 2D images and 3D point cloud data for cover plate identification and processing. A disassembly robot, equipped with a torque-adjustable robotic arm and a laser cutter, automated the disassembly of various covers. The experimental setup simulated an industrial environment, featuring transformer covers of different sizes and conditions, with 50 samples each of screw-fastened and soldered covers. Each sample underwent surface treatment (such as rust or oil) or was sourced from actual end-of-life transformers to accurately replicate real industrial conditions.  
To fully assess the disassembly performance of the fuel tank cover, the following evaluation metrics were employed in the experiments:  
1. Recognition accuracy: Measures the system's ability to classify screw-fastened and welded covers accurately.
2. Disassembly time: Total time taken from identification to the completion of disassembly.
3. Success rate: The rate of successful disassembly without causing damage.
4. Safety assessment: Evaluates the impact on transformer core components during the disassembly process.  
The results are illustrated in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & **Recognition** & **Disassembly** & \multirow{2}{*}{**Success Rate**} & **Security** \\  & **Accuracy** & **Time** & & **Assessment** \\ \hline Screw-fastened cover & 99\% & 186.3 s & 98\% & Undamaged \\ Welded cover & 99\% & 265.4 s & 96\% & Undamaged \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results of disassembling the fuel tank cover.  
Figure 14: Transformer disassembly area in the new established disassembly factory.  
The recognition accuracy reached 99% for both screw-fastened and welded covers, demonstrating that the combination of RGB-D cameras and multimodal fusion technology effectively mitigates the adverse effects of contamination, such as oil and rust, on recognition performance. By leveraging both the texture information from 2D images and the depth data from 3D point clouds, the system maintains high accuracy even in complex industrial environments.  
The average disassembly time for screw-fastened covers was 186.3 s, compared to 265.4 s for welded covers. The increased time for welded covers stems from the complexity of cutting through welded joints, necessitating a more intricate process. While screw-fastened covers allow for straightforward torque control with a robotic arm, welded covers involve laser cutting, edge recognition, and detailed cutting path planning, making the operation more time-consuming.  
The success rates for disassembling screw-fastened and welded covers were 98% and 96%, respectively. The higher success rate for screw-fastened covers was due to precise torque control of the robotic arm, minimizing damage during disassembly. In contrast, the slightly lower success rate for welded covers was linked to issues like melt adhesion from localized melting of the joints. Enhancing laser cutting precision and optimizing laser power could further improve outcomes. In safety assessments, no damage occurred to the transformer core during the disassembly of either cover type, highlighting effective safety measures and the use of an expansion mask to protect critical components. Overall, the system demonstrated strong performance in recognition accuracy, efficiency, safety, and success rate, confirming the feasibility of the proposed approach.