Artificial Intelligence and Image Processing Based Part Feeding Control in a Robot Cell

In this study, an artificial intelligence-assisted image processing system was developed to prevent errors in part feeding processes within an industrial robot cell. Using the YOLOv7-tiny model, accurate detection of parts was ensured, enabling effective quality control. While PLC communication was established via the ModBus protocol, the system hardware included an NVIDIA JETSON AGX ORIN, a BASLER acA2500-60uc camera, and a Raspberry Pi WaveShare monitor. A total of 2400 data samples were used for model training, achieving an accuracy rate of 98.07%. The developed system minimized human errors by preventing incorrect part feeding issues and significantly improved efficiency in production processes. Notably, the system's superior accuracy and processing speed demonstrated its suitability for real-time applications. In conclusion, this study highlights the effective implementation of artificial intelligence and image processing techniques in industrial manufacturing processes.