- Tytuł:
- Defect Detection Using Deep Learning-Based YOLOv3 in Cross-Sectional Image of Additive Manufacturing
- Autorzy:
-
Choi, Byungjoo
Choi, Yongjun
Lee, Moon-Gu
Kim, Jung-Sub
Lee, Sang-Won
Jeon, Yongho - Powiązania:
- https://bibliotekanauki.pl/articles/2048889.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
additive manufacturing
deposition defect
data augmentation
YOLOv3
object detection - Opis:
- Deposition defects like porosity, crack and lack of fusion in additive manufacturing process is a major obstacle to commercialization of the process. Thus, metallurgical microscopy analysis has been mainly conducted to optimize process conditions by detecting and investigating the defects. However, these defect detection methods indicate a deviation from the operator’s experience. In this study, artificial intelligence based YOLOv3 of object detection algorithm was applied to avoid the human dependency. The algorithm aims to automatically find and label the defects. To enable the aim, 80 training images and 20 verification images were prepared, and they were amplified into 640 training images and 160 verification images using augmentation algorithm of rotation, movement and scale down, randomly. To evaluate the performance of the algorithm, total loss was derived as the sum of localization loss, confidence loss, and classification loss. In the training process, the total loss was 8.672 for the initial 100 sample images. However, the total loss was reduced to 5.841 after training with additional 800 images. For the verification of the proposed method, new defect images were input and then the mean Average Precision (mAP) in terms of precision and recall was 0.3795. Therefore, the detection performance with high accuracy can be applied to industry for avoiding human errors.
- Źródło:
-
Archives of Metallurgy and Materials; 2021, 66, 4; 1037-1041
1733-3490 - Pojawia się w:
- Archives of Metallurgy and Materials
- Dostawca treści:
- Biblioteka Nauki
Artykuł