An in situ surface defect detection method based on improved you only look once algorithm for wire and arc additive manufacturing
ISSN: 1355-2546
Article publication date: 15 November 2022
Issue publication date: 2 May 2023
Abstract
Purpose
Wire and arc additive manufacturing (WAAM) is a widely used advanced manufacturing technology. If the surface defects occurred during welding process cannot be detected and repaired in time, it will form the internal defects. To address this problem, this study aims to develop an in situ monitoring system for the welding process with a high-dynamic range imaging (HDR) melt pool camera.
Design/methodology/approach
An improved you only look once version 3 (YOLOv3) model was proposed for online surface defects detection and classification. In this paper, improvements were mainly made in the bounding box clustering algorithm, bounding box loss function, classification loss function and network structure.
Findings
The results showed that the improved model outperforms the Faster regions with convolutional neural network features, single shot multibox detector, RetinaNet and YOLOv3 models with mAP value of 98.0% and a recognition rate of 59 frames per second. And it was indicated that the improved YOLOv3 model satisfied the requirements of real-time monitoring well in both efficiency and accuracy.
Originality/value
Experimental results show that the improved YOLOv3 model can solve the problem of poor performance of traditional defect detection models and other deep learning models. And the proposed model can meet the requirements of WAAM quality monitoring.
Keywords
Acknowledgements
This work is supported by the Ministry of Science and Technique of the People’s Republic China under the National Key R&D Program of China [Grant no. 2019YFB1311100] and Ministry of Industry and Information Technology of the People’s Republic China under Special Research Project of Chinese Civil Aircraft [Grant no. MJ-2017-G-60].
Competing interests: The authors declare no competing interests.
Citation
Wu, J., Huang, C., Li, Z., Li, R., Wang, G. and Zhang, H. (2023), "An
Publisher
:Emerald Publishing Limited
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