## 1 Introduction  
Transformers play a crucial role in transmission and distribution systems, and their reliable operation is essential for grid stability. However, with aging equipment and increasing maintenance demands, transformer disassembly has become progressively more complex. Traditional manual methods are not only inefficient but also hazardous and environmentally damaging, underscoring the need for safer, automated solutions [1, 2, 3]. Current technologies still rely heavily on manual labor, requiring skilled operators and presenting safety risks, such as mishandling bolts or fasteners, which can lead to injury. Furthermore, disassembling welded covers requires precise cutting and handling, and errors in identifying key components can damage critical parts [4]. Enhancing disassembly efficiency while ensuring safety remains a key challenge in transformer recycling and maintenance [5, 6]. Significant progress has been made in the automation of transformer disassembly, particularly in the context of electric vehicle battery recycling. With the advancement of deep learning techniques in the industry [7, 8, 9], researchers have developed multimodal image fusion and intelligent control technologies to enhance component identification and operational efficiency [10]. Cognitive robotics, which integrates vision systems with intelligent planning, enables robots to adapt dynamically in real-time, improving system flexibility [11]. Robotic arms, combined with deep learning, are increasingly employed for disassembling industrial transformers, allowing for the precise targeting of bolts and welded parts [12]. Additionally, multimodal fusion techniques, which combine 2D images with 3D point cloud data from RGB-D cameras, significantly enhance target recognition in complex environments [13]. Automated and semi-automated wire stripping machines have also been introduced to improve the recycling of waste wires. For example, pneumatic automatic wire strippers can increase efficiency by 25% compared to manual methods [14]. Researchers have also developed a multi-diameter cable-cutting machine for cables with varying diameters. Experiments have shown that adjusting the pusher extension distance improves both cutting speed and quality, enhancing the efficiency and performance of industrial cable processing [15]. Recent advancements have led to the development of automated transformer disassembly methods that leverage machine learning and image recognition technologies. Researchers have designed robot-based disassembly systems, primarily for recycling waste transformers, which integrate multimodal data fusion to identify and process complex structures such as bolts and welds [16]. The incorporation of IoT and big data technologies has also enabled the remote monitoring and dynamic operation of these systems, facilitating real-time analysis and optimization of the disassembly process [17; 18; 19]. For electronic waste, intelligent robotic and vision-based solutions use multimodal data to accurately identify hazardous and recyclable materials, efficiently processing them on automated platforms [20]. Moreover, multimodal fusion technology, combining RGB-D cameras with deep learning models, improves disassembly automation by accurately detecting and evaluating critical components like welds and bolts [21]. To address the challenges of recycling small-diameter waste transformer wires--where traditional mechanical knives wear quickly and lack automation, and methods like incineration or chemical stripping lead to environmental pollution--a device utilizing ultrasonic cutting technology has been developed. This approach offers high efficiency and minimal wear, providing an effective solution for stripping waste transformer wires [15]. Despite global advancements in automated transformer disassembly, several challenges remain.