A machine vision assisted system for fluorescent magnetic particle inspection of railway wheelsets

Ma, Tao; Sun, Zengqi; Zhang, Wenzeng; Chen, Qiang

doi:10.1063/1.4940615

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…A substantial number of works have been found in the literature on the MPI topic. For instance, [4] shows an industrial application of a conventional computer vision algorithm on a rails wheelset. The non-standalone system built around a camera and a small lighting system must be coupled with an external PC for image processing, moreover, PoD results are not given and only qualitative examples are shown.…”

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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show abstract

“…Ma et al 4 used a magnetic particle inspection machine equipped with an industrial camera with a filter to detect cracks on the surface of train wheels. The authors performed image processing, including top-hat processing and Otsu's adaptive binarization, on quantitative quality indicators and actual defects and subsequently extracted the altered image as a feature to determine the size and location of the defect.…”

Section: Introductionmentioning

confidence: 99%

Image feature analysis for magnetic particle inspection of forging defects

Hsu

2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Magnetic particle inspection is typically used to detect the magnetic leakage caused by defects. This method is mainly used to detect the surface and subsurface defects of ferromagnetic materials. The conventional detection method involves inspectors performing visual inspection under high-power ultraviolet light. However, the intense ultraviolet light can easily damage the eyes of the inspectors. Furthermore, the aforementioned process is not only time consuming but also susceptible to human errors. Therefore, this study developed an automated optical inspection system to perform magnetic particle inspection. Analysis of several image features revealed that a contour compactness between four and five can be used to distinguish defective and non-defective features effectively. The defect identification ability obtained with several input combinations of image features for neural networks was analyzed. The results revealed that a high identification ability can be achieved for defective features when the input combination of area, mean width, and compactness is used.

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Automatic Optical Inspection for Magnetic Particle Detection of Forging Defects

Hsu

et al. 2019

Advances in Engineering Research and Application

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A machine vision assisted system for fluorescent magnetic particle inspection of railway wheelsets

Cited by 8 publications

References 8 publications

Industrial Application of AI-Based Assistive Magnetic Particle Inspection

Industrial Application of AI-Based Assistive Magnetic Particle Inspection

Image feature analysis for magnetic particle inspection of forging defects

Automatic Optical Inspection for Magnetic Particle Detection of Forging Defects

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