A defect detection system for wire arc additive manufacturing using incremental learning

Li, Yuxing; Polden, Joseph; Pan, Zengxi; Cui, Junyi; Xia, Chunyang; He, Fengyang; Mu, Haochen; Li, Huijun; Wang, Lei

doi:10.1016/j.jii.2021.100291

Cited by 27 publications

(13 citation statements)

References 29 publications

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“…The work presented in this paper forms an essential component in a wider intelligent monitoring system for WAAM. It will provide image-based defect detection capabilities that can be integrated with other defect detection subsystems, such as the electrical signals' monitoring system presented in [54]. Future work will focus on embedding the model with a functional graphical user interface (GUI), which will assist technicians in the practical task of detecting the presence of anomalies.…”

Section: Discussionmentioning

confidence: 99%

Towards intelligent monitoring system in wire arc additive manufacturing: a surface anomaly detector on a small dataset

Polden

et al. 2022

Int J Adv Manuf Technol

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Rapid developments in artificial intelligence and image processing have presented many new opportunities for defect detection in manufacturing processes. In this work, an intelligent image processing system has been developed to monitor inter-layer deposition quality during a wire arc additive manufacturing (WAAM) process. This system reveals the feasibility and future potential of using computer vision knowledge in WAAM. Information produced from this system is to be used in conjunction with other quality monitoring systems to verify the quality of fabricated components. It is tailored to identify the presence of defects relating to lack of fusion and voids immediately after the deposition of a given layer. The image processing system is built upon the YOLOv3 architecture and through moderate changes on anchor settings and achieves 53% precision on surface anomaly detection and 100% accuracy in identifying the fabricated components’ location, providing a prerequisite for high-precision assessment of welding quality. The work presented in this paper presents an inter-layer vision-based defect monitoring system in WAAM and serves to highlight the feasibility of developing such intelligent computer vision systems for monitoring the WAAM process for defects.

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Section: Discussionmentioning

confidence: 99%

Towards intelligent monitoring system in wire arc additive manufacturing: a surface anomaly detector on a small dataset

Polden

et al. 2022

Int J Adv Manuf Technol

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“…The work presented in this paper forms an essential component in a wider intelligent monitoring system for WAAM. It will provide image-based defect detection capabilities that can be integrated with other defect detection sub-systems, such as the electrical signals monitoring system presented in [47]. Future work will focus on embedding the model with a functional Graphical User Interface (GUI), which will assist technicians in the practical task of detecting the presence of anomalies.…”

Section: Discussionmentioning

confidence: 99%

Towards Intelligent Monitoring System in Wire Arc Additive Manufacturing: A Surface Anomaly Detector on a Small Dataset

Polden

et al. 2021

Preprint

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Rapid developments in artificial intelligence and image processing have presented many new opportunities for defect detection in manufacturing processes. In this work, an intelligent image processing system has been developed to monitor inter-layer deposition quality during a Wire Arc Additive Manufacturing (WAAM) process. Information produced from this system is to be used in conjunction with other quality monitoring systems to verify the quality of fabricated components. It is tailored to identify the presence of defects relating to lack-of-fusion and voids immediately after the deposition of a given layer. The image processing system is built upon the YOLOv3 architecture and through moderate changes on anchor settings, achieves 53% precision on surface anomaly detection and 100% accuracy in identifying the fabricated components’ location, providing a prerequisite for high precision assessment of welding quality. The work presented in this paper presents an inter-layer vision-based defect monitoring system in WAAM and serves to highlight the feasibility of developing such intelligent computer vision systems for monitoring the WAAM process for defects.

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“…[2][3][4][5][6][7][8][9][10] And some researchers have designed a string of defect detection systems based on machine vision methods for specific products and defects. [11][12][13][14][15][16][17][18][19][20][21] Textile fabrics defect was detected via optimal Gabor filter by Jing et al 2 In, 3 a method for detecting fabric defects using a thermal camera was presented. A method based on principal component analysis was proposed by Kazim to achieve dimensionality reduction-based classification of fleece fabric-based images.…”

Section: Introductionmentioning

confidence: 99%

“…A collaborative cloud edge fabric defect detection system based on the MobileNetV-SSDLite method was presented in, 20 which improved the detection accuracy of small defects and complex background images in resource-constrained situations. A wire arc additive manufacturing defect detection system based on incremental learning was proposed by Li et al 21 As the needs of society are constantly being updated, the new composite materials are playing an increasingly important role in everyday life. Such as glass tube yarn, also known as glass fiber spun yarn, is a yarn body formed by winding electronic grade glass fiber yarn around a tube body for ease of packaging and transportation.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A defect detection system of glass tube yarn based on machine vision

Bao

Jing

Xie

2023

Journal of Industrial Textiles

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Tube yarn is also called glass fiber spun yarn. Due to the excellent properties of glass fiber, various industrial products based on glass fiber are used in a variety of industries. As the most obvious factor affecting the quality of products, quality detection of glass fiber yarns is very important for companies. Due to traditional defect detection relies on the experience and subjective factors of workers, which makes many different views on the same defect. Some traditional methods limit the solution to specific types of defects and do not accurately detect various defects. In this paper, we propose a comprehensive defect detection system of tube yarn via combining machine vision and deep learning methods. In whole system, we inspect the weight through a weight sensor firstly. Then, we propose a multi-scale cross-fusion attention module to improve the MobileNetV2, and combine with machine vision image feature extraction method for hairiness detection. Finally, the modified MobileNetV2 network is used as the backbone of YOLOX network, making the YOLOX is lighter and achieve more efficiently stain detection of tube yarn. Then, the detection results are used to determine whether the glass tube yarn has passed. In addition, we establish an effective and sufficient amount of tube yarn defects dataset. The experimental results show that the proposed hairiness detection algorithm achieve 96% accuracy with 160+ FPS, and the surface stain detection algorithm achieve 0.89 mAP with 71+ FPS on the tube yarn dataset. The system is efficient, precise, and can be applied to actual production.

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A defect detection system for wire arc additive manufacturing using incremental learning

Cited by 27 publications

References 29 publications

Towards intelligent monitoring system in wire arc additive manufacturing: a surface anomaly detector on a small dataset

Towards intelligent monitoring system in wire arc additive manufacturing: a surface anomaly detector on a small dataset

Towards Intelligent Monitoring System in Wire Arc Additive Manufacturing: A Surface Anomaly Detector on a Small Dataset

A defect detection system of glass tube yarn based on machine vision

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