An Improved 3D Printing Extrusion Defect Detection Method Based On YOLO-v8

Abstract

Additive manufacturing (AM) technology finds widespread use in consumer and industrial manufacturing. However, large-scale 3D printers in the manufacturing industry encounter challenges like extended printing times, high nozzle flow, and maintaining flow stability. Addressing these issues necessitates thorough research and optimization of 3D printers to enhance printing efficiency and stability. This study proposes a real-time monitoring system for identifying flow defects in large 3D printers using machine vision. The system comprises a camera capturing extrusion images, a computer processing real-time screens, and a lower computer within the monitoring system designed for real-time monitoring and correction of flow quality in large 3D printers. Defects are detected and image data captured by adjusting the extrusion speed, while an attention mechanism module optimizes the YOLOv8 model. The model is trained for deep learning using mAP50 as the performance evaluation index, achieving a mean average precision (mAP) of 92.1%. Subsequently, the optimized model is deployed on a host computer. Upon detecting flow defects, it sends commands to a Raspberry Pi, generating G-code commands to adjust extrusion speed and relay them to the 3D printer. This enables real-time monitoring and automatic calibration of the printing process. The study presents an effective solution for real-time monitoring and calibration in closed-loop processes within additive manufacturing.