Research on an Improved Metal Surface Defect Detection Sensor Based on a 3D RFID Tag Antenna

Li, Qiang; Chen, Jianbin; Zhao, Liang

doi:10.1155/2020/8824091

Cited by 5 publications

(3 citation statements)

References 34 publications

(37 reference statements)

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“…In [50], a multi-directional expansion of metal defects is studied, where a 3D patchbend dipole operating at 915 MHz was used. The simulation process was performed via Ansys HFSS.…”

Section: Figurementioning

confidence: 99%

A Past, Present, and Prospective Review on Microwave Nondestructive Evaluation of Composite Coatings

et al. 2021

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Recent years have witnessed an increase in the use of composite coatings for numerous applications, including aerospace, aircraft, and maritime vessels. These materials owe this popularity surge to the superior strength, weight, stiffness, and electrical insulation they exhibit over conventional substances, such as metals. The growing demand for such materials is accompanied by the inevitable need for fast, accurate, and affordable nondestructive testing techniques to reveal any possible defects within the coatings or any defects under coating. However, typical nondestructive testing (NDT) techniques such as ultrasonic testing (UT), infrared thermography (IRT), eddy current testing (ECT), and laser shearography (LS) have failed to provide successful results when inspecting composite coatings. Consequently, microwave NDT techniques have emerged to compensate for the shortcomings of traditional NDT approaches. Numerous microwave NDT methods have been reported for composite coatings inspection. Although existing microwave NDT methods have shown successful inspection of composite coatings, they often face several challenges, such as low spatial image quality and extensive data interpretation. Nevertheless, many of these limitations can be addressed by utilizing microwave NDT techniques with modern technologies such as soft computing. Artificially intelligent techniques have greatly enhanced the reliability and accuracy of microwave NDT techniques. This paper reviews various traditional NDT techniques and their limitations in inspecting composite coatings. In addition, the article includes a detailed review of several microwave NDT techniques and their benefits in evaluating composite coatings. The paper also highlights the advantages of using the recently reported microwave NDT approaches employing artificial intelligence approaches. This review demonstrates that microwave NDT techniques in conjunction with artificial intelligence approaches have excellent prospects for further enhancing composite coatings inspection and assessment efficiency. The review aimed to provide the reader with a comprehensive overview of most NDT techniques used for composite materials alongside their most salient features.

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“…In [50], a multi-directional expansion of metal defects is studied, where a 3D patchbend dipole operating at 915 MHz was used. The simulation process was performed via Ansys HFSS.…”

Section: Figurementioning

confidence: 99%

A Past, Present, and Prospective Review on Microwave Nondestructive Evaluation of Composite Coatings

et al. 2021

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“…Literature [20] tested the performance of a sensor integrating wireless sensing technology and intelligent monitoring technology for workpiece structure identification based on simulation experiments and found that this sensor accurately identifies and locates workpiece defects. Literature [21] confirms the good performance of the metal defect identification system based on graphical alignment algorithms and defect identification module through the feedback from the use of the metal defect identification system in three real cases, which can achieve the goal of accurately locating the position of metal defects.…”

Section: Introductionmentioning

confidence: 99%

Laser Descaling Area Recognition Method Based on LabVIEW and Machine Vision

Gao,

Zhao,

Shangguan

et al. 2024

Applied Mathematics and Nonlinear Sciences

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Automated laser descaling represents a crucial advancement in marine maintenance, particularly in localized corrosion treatment. This research utilizes AH32 marine steel for corrosion testing, applying color feature extraction and the OTSU thresholding method, refined with morphological processing, to accurately delineate descaling areas. We have engineered a laser descaling protocol with an impressive 97% recognition accuracy through the innovative application of LabVIEW technology combined with machine vision. Implementing this technology promises enhanced corrosion resistance for marine vessels, setting a new standard for maintenance efficiency and quality.

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“…The number, degree, and distribution of surface defects areas are significant factors to determine the quality of industrial metal planar materials. The damage detection methods based on vibro-acoustic modulation [ 1 ], wireless sensing technology [ 2 ] and other different principles have been researched for a long time. However, the computer-vision-based surface defect detection methods are the most commonly used to find and locate the abnormal areas on the image surface due to their advantages of low cost, easy operation, and superior performance, etc.…”

Section: Introductionmentioning

confidence: 99%

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Fang

Luo

Zhou

et al. 2020

Sensors

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The computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical industry. The high standard of planar surface quality in the metal manufacturing industry requires that the performance of an automated visual inspection system and its algorithms are constantly improved. This paper attempts to present a comprehensive survey on both two-dimensional and three-dimensional surface defect detection technologies based on reviewing over 160 publications for some typical metal planar material products of steel, aluminum, copper plates and strips. According to the algorithm properties as well as the image features, the existing two-dimensional methodologies are categorized into four groups: statistical, spectral, model, and machine learning-based methods. On the basis of three-dimensional data acquisition, the three-dimensional technologies are divided into stereoscopic vision, photometric stereo, laser scanner, and structured light measurement methods. These classical algorithms and emerging methods are introduced, analyzed, and compared in this review. Finally, the remaining challenges and future research trends of visual defect detection are discussed and forecasted at an abstract level.

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Research on an Improved Metal Surface Defect Detection Sensor Based on a 3D RFID Tag Antenna

Cited by 5 publications

References 34 publications

A Past, Present, and Prospective Review on Microwave Nondestructive Evaluation of Composite Coatings

A Past, Present, and Prospective Review on Microwave Nondestructive Evaluation of Composite Coatings

Laser Descaling Area Recognition Method Based on LabVIEW and Machine Vision

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

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