Design of Eddy Current Testing Probe for Surface Defect Evaluation

Saari, Mohd Mawardi; Nadzri, Nurul A’in; Aiman, M. H.; Ishak, M.; Sakai, Kenji; Kiwa, Toshihiko; Tsukada, Keiji

doi:10.15282/ijame.16.1.2019.19.0481

Cited by 16 publications

(3 citation statements)

References 33 publications

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“…With the breakthrough of CCD (charge-coupled device) camera and related hardware, while computer technology gradually developed and gradually appeared in industries, online machine vision-based steel plate inspection became popular. Figure 1 shows the working process and the role of each part: the steel plate to be inspected moves below the light source which provides additional light to remove the influence of background light and provide clear images collected by the camera which transmits them to the computer [ 12 ]. The computer analyzes and processes the images in real time according to the existing model and framework to assess the grade or defect situation of the steel plate surface.…”

Section: Related Workmentioning

confidence: 99%

Multilayer Feature Extraction of AGCN on Surface Defect Detection of Steel Plates

Zhang

Cui

Liu

2022

Computational Intelligence and Neuroscience

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The development of industry is inseparable from the support of steel materials, and the modern industry has increasingly high requirements for the quality of steel plates. But the process of steel plate production produces many types of defects, such as roll marks, scratches, and scars. These defects will directly affect the quality and performance of the steel plate, so it is necessary to effectively detect them. Steel plate surface defects are characterized by their types, shape, and size: the same defect can have different morphologies, and similarities can exist between different defects. In this paper, industrial steel plate surface defect samples are analyzed, and a sample set is established by screening the collected defect images. Then, annotation and classification are performed. A multilayer feature extraction framework is developed in experiments to train a neural network on the sample set of defects. To address the problems of low automation, slow detection speed, and low accuracy of the traditional defect detection methods, the attention graph convolution network (AGCN) is investigated in this paper. Firstly, faster R-CNN is used as the basic network model for defect detection, and the visual features are jointly refined by combining attention mechanism and graph convolution neural network. The latter network enriches the contextual information in the visual features of steel plates and explores the semantic association between vision and defect types for different kinds of defects using the attention mechanism to achieve intelligent detection of defects, thus enabling our method to meet the practical needs of steel plate production.

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Section: Related Workmentioning

confidence: 99%

Multilayer Feature Extraction of AGCN on Surface Defect Detection of Steel Plates

Zhang

Cui

Liu

2022

Computational Intelligence and Neuroscience

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“…An emerging measurement technique based on magnetic field disturbance in the presence of defects is considered a high potential for early detection of cracks within a steel structure, owing to steel's conductive and ferromagnetic characteristics. Not only that, the safe, rapid, and non-contact nature of the magnetic field measurement technique further justify the convenience of using this method [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Flux Leakage and Eddy Current Signals Induced by Submillimeter Backside Slits on Carbon Steel Plate Using a Low-Field AMR Differential Magnetic Probe

et al. 2021

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Magnetic Flux Leakage (MFL) and Eddy Current Testing (ECT) are commonly employed as the non-destructive evaluation (NDE) techniques used to detect defects within the steel. The MFL technique is advantageous in terms of deep defects detection, while the ECT technique excels in providing dense information regarding defects. In this work, artificial MFL and eddy current (EC) signals in ferromagnetic materials are studied, and an experimental magnetic probe that utilizes both techniques is developed for signal verification. The separation between MFL and EC signals is achieved by utilizing the phase-sensitive detection technique, implementing a dynamic referencing method as opposed to the conventional static phase referencing. A finite element method (FEM) based simulation is employed to study and verify the MFL and EC signals measured by the proposed magnetic probe. The proposed magnetic probe features highly sensitive anisotropic magneto-resistive sensors capable of measuring the MFL and EC signals induced by artificial slits of varying depths engraved onto a 2-mm carbon steel plate. Finite element simulations indicate different flux leakage patterns and eddy current signals detected in the vicinity of the back-side slits. A good agreement is observed between the simulated and the measured MFL and EC signals for the optimized frequency range of 110-210 Hz with the corresponding Lissajous curve for the detection of submillimeter back-side slits. The study has shown that the combination of MFL and EC signals can be successfully captured by an appropriate magnetic probe for an enhanced detection performance of back-side defects in ferromagnetic materials.

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“…The first at-home personal desktop 3D printer emerged in 2005, which allow individuals to experiment with various innovative product design as well as to create successful prototypes prior to actual manufacturing processes. For example, prototypes of hydrokinetic turbine blade [2], recirculation channel of turbocharger compressor [3], and Eddy current testing probe [4] could be produced via 3D printer based on the conducted numerical simulations and composed design in order to study it manufacturability and producibility [5]. It is due to the fact that 3D printing is generally faster, affordable and easier to use compared to other quick prototyping technologies.…”

Section: Introductionmentioning

confidence: 99%

Tensile Characteristics of Low Density Infill Patterns for Mass Reduction of 3D Printed Polylactic Parts

Saniman¹,

Hashim²,

Mohammad³

et al. 2020

IJAME

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Various infill patterns are introduced in 3D printing to generate low density objects that leads to reduced cost and fabrication time through mass reduction. However, as a trade-off, the strength of the 3D printed component is uncertain. Confusions arise in determining the infill pattern with highest value of tensile strength since most studies limited only to rectilinear, honeycomb, and concentric infill patterns. As consequences, there are very little information on rarely used infill patterns such as Hilbert curve, Archimedean cord and octagram spiral. Therefore, the purpose of this research is to investigate and compare the tensile strength and strain of all infill patterns in mass reduction of 3D printed components experimentally. Following ASTM D638 type III standard, ten tensile test specimens of each infill patterns with 20% density were printed with an FFF 3D printer and were then tested. It was found that Archimedean cords infill pattern had the highest specific tensile strength of 33.23×103 MPa∙mm3/g which made it as the optimum infill pattern for the mass reduction of 3D printed parts with a high tensile strength. On the other hand, having the highest specific tensile strain of 18.21×103 %∙mm3/g, concentric infill pattern was found to be more suitable for producing lightweight parts with a higher elongation before break. Additionally, Hilbert curve infill was the worst selection for mass reduction since it had the lowest values of specific tensile strength and specific strain of 19.80×103 MPa∙mm3/g and 8.34 %∙mm3/g, respectively. Nevertheless, the trends of tensile strength and strain of all six infill patterns had been obtained, especially for rarely investigated infill patterns of Archimedean cords, octagram spiral, and Hilbert curve. Specifically, the trend from the strongest to the weakest (in % compared to solid) for specific tensile strength is rectilinear (38.57%), Archimedean chords (37.29%), concentric (36.57%), octagram spiral (34.79%), honeycomb (27.84%), and Hilbert curve (22.25%), while for specific strain is concentric (102.6%), octagram spiral (83.94%), rectilinear (78.22%), Archimedean cords (77.99%), honeycomb (54.84%), and Hilbert curve (45.14%).

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Design of Eddy Current Testing Probe for Surface Defect Evaluation

Cited by 16 publications

References 33 publications

Multilayer Feature Extraction of AGCN on Surface Defect Detection of Steel Plates

Multilayer Feature Extraction of AGCN on Surface Defect Detection of Steel Plates

Extraction of Flux Leakage and Eddy Current Signals Induced by Submillimeter Backside Slits on Carbon Steel Plate Using a Low-Field AMR Differential Magnetic Probe

Tensile Characteristics of Low Density Infill Patterns for Mass Reduction of 3D Printed Polylactic Parts

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