A study of defect detection in the first layer of FDM3D printing based on improved YOLOv8s

Zihao Fu; Zhijiang Zuo; Yi Zhang

doi:10.1117/12.3051354

18 November 2024 A study of defect detection in the first layer of FDM3D printing based on improved YOLOv8s

Zihao Fu, Zhijiang Zuo, Yi Zhang

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134033Q (2024) https://doi.org/10.1117/12.3051354
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

Fused Deposition Modeling (FDM) is one of the most widely used additive manufacturing processes, but its printing process defects such as first-layer defects, drawing, wrinkling and other printing failure problems will often occur, and the printing first-layer defects are the most common and the most influential kind of defects in 3D printing process. In this study, for this first layer defect identification problem in 3D printing, a 3D printing first layer defect detection method based on improved YOLOv8s is proposed, which adopts the EfficientViT model as the backbone network, and improves the diversity of attention while reducing the number of overall parameters in the model. Secondly, MPDIoU is used to replace the CIoU of the original model for the problem of bounding box regression loss. to improve the detection performance of the model, it is experimentally verified that the improved model improves Precision and Recall by 3.5% and mAP 50 by 3.3% in the first-layer defect detection compared with the original YOLOv8s model, respectively. The model size is reduced by 47.4%, which has the advantages of high accuracy in first layer defect recognition and less memory consumption.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zihao Fu, Zhijiang Zuo, and Yi Zhang "A study of defect detection in the first layer of FDM3D printing based on improved YOLOv8s", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134033Q (18 November 2024); https://doi.org/10.1117/12.3051354

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available