Toward Accurate Fused Deposition Modeling 3D Printer Fault Detection Using Improved YOLOv8 With Hyperparameter Optimization

This research article presents an enhanced YOLOv8 model with an additional feature extraction layer integrated into the traditional YOLOv8 architecture to improve fault detection performance in smart additive manufacturing, specifically for FDM 3D printers. Hyperparameter optimization techniques are employed to ensure the model is trained with optimal input and batch size configurations. The findings demonstrate that the additional module successfully enhances the model’s performance in detecting faults during the FDM 3D printing process. The best results are achieved using the YOLOv8s model with an image input size of 640 and a batch size of 16, achieving a mAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>val</i></sub> (50-95) of 89.7%. Despite the increased complexity from additional layers, there is a favorable trade-off between performance and complexity. Furthermore, a testbed implementation is conducted to validate the model’s performance in a real-world setting, showing that the fault detection latency remains insignificant even with multiple Raspberry Pi clients. Overall, this research provides insights into improving fault detection in smart additive manufacturing and highlights the effectiveness of the proposed YOLOv8 model with additional extraction layers.