Development of a new type of automatic magnetic particle inspection wall-climbing robot

Li, Jun; Yu, Hanlin; Linbo, Mei; Han, Bo

doi:10.1177/16878140211047339

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…As stated in this article, optical filters are mandatory, and thus were used with the PARADES machine. A mobile machine based on a robot is depicted in [6] for use with MPI control on wide parts. It shows an interesting embedded magnetic excitation material as well as an MPI suspension dispenser.…”

Section: Related Workmentioning

confidence: 99%

Industrial Application of AI-Based Assistive Magnetic Particle Inspection

Baumeyer,

Chatoux,

Pelletier

et al. 2024

Applied Sciences

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Magnetic Particle Inspection (MPI) is one of the most used methods in Non-Destructive Testing (NDT), allowing precise and robust defect detection on industrial-grade manufactured parts. However, human controllers perform this task in full black environments under UV-A lighting only (with safety glasses) and use chemical products in a confined environment. Those constraints tends to lower control performance and increase stress and fatigue. As a solution, we propose an AI-based assistive machine (called "PARADES") inside the hazardous environment, remotely manipulated by a human operator, outside of the confined area, in cleaner and safer conditions. This paper focuses on the development of a complete industrial-grade AI machine, both in terms of hardware and software. The result is a standalone assistive AI-based vision system, plug-and-play and controller-friendly, which only needs the usual power supply 230 V plug that detects defects and measures defect length. In conclusion, the PARADES machines address for the first time the problem of occupational health in MPI with an industrial standalone machine which can work on several parts and be integrated into current production lines. Providing cleaner and healthier working conditions for operators will invariably lead to increased quality of detection. These results suggest that it would be beneficial to spread this kind of AI-based assistive technology in NDT, in particular MPI, but also in Fluorescent Penetrant Testing (FPT) or in visual inspection.

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Section: Related Workmentioning

confidence: 99%

Industrial Application of AI-Based Assistive Magnetic Particle Inspection

Baumeyer,

Chatoux,

Pelletier

et al. 2024

Applied Sciences

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“…To provide a more effective quantitative assessment of the improved model's performance, we chose precision (Precision), mean Average Precision (mAP), frames per second (FPS), floating point operation rate (FLOPs), and parameter quantities as model evaluation indices during the research. Their expressions are formula (7), formula (8), formula (9) and formula (10) respectively.…”

Section: Bperformance Metricsmentioning

confidence: 99%

“…Traditional automobile brake disc defect detection methods mainly include visual inspection, measurement, magnetic particle inspection [6,7], ultrasonic flaw detection [8,9], etc. Although these methods are simple and convenient, they also have certain limits.…”

Section: Introductionmentioning

confidence: 99%

Car Brake Disc Surface Defect Detection Based on Improved YOLOv5

Guo,

Zhang,

Dong

2024

IEEE Access

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Recycling and remanufacturing used automobile brake discs is an important measure to reduce environmental pollution and reduce production and manufacturing costs. Previous recycling detection methods mainly relied on manual visual inspection, which had problems such as low inspection efficiency and high missed detection rate. To address this problem, an improved YOLOv5 method for detecting surface defects on automobile brake discs is proposed. We choose the lightweight feature extraction network CloFormer to replace the original YOLOv5 basic network to improve the detection speed while reducing parameter complexity. At the same time, we introduce an improved GAN (Generative Adversarial Networks) network branch and use auxiliary classifiers to improve classification accuracy and reduce the missed detection rate. In addition, a genetic algorithm is used for bias optimization in the model fusion stage to improve the overall detection performance of the model. Afterwards, the improved model was compared and verified with similar algorithms on public data sets. Its recognition accuracy was significantly improved by 40.56%, and its mAP gain was 17.96%. It also achieved an FPS improvement of 38.72% and a parameter decline of 4.12%. Finally, when verified in real scenarios, the average accuracy rate was as high as 90.5%, the missed detection rate was 1.375%, and the false detection rate was reduced to 1.5%, achieving the expected results of the experiment. This method can provide a reference for the defect detection method of automobile brake disc recycling.

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