Research on detection equipment system for key components of new energy vehicles based on Digital Twin and 3D vision

Artificial intelligence plays a critical role in high-precision inspection of new energy vehicle components, addressing micro-defect identification and 3D geometric feature quantification to ensure driving safety. To achieve accurate defect detection while balancing computational efficiency, this study integrates Digital Twin and 3D vision technologies: high-fidelity defect samples are generated via virtual-physical interaction platforms, with point cloud data compressed using voxelization algorithms. A dedicated component inspection system was developed. Experimental validation against Canny edge detection and morphological gradient methods demonstrated performance superiority. System efficacy was evaluated using actual component data, showing the improved algorithm reduced average relative error to 3.5%, with crack and holeposition errors at 3.15% and 2.78% respectively—70.8% lower than conventional methods. The system supports 12 component types and maintained 120-hour fault-free operation under high temperatures. Through virtual-physical data synergy and algorithm optimization, this research delivers high-precision defect detection, offering a referential technical solution for new energy vehicle quality control.