Improvement on Defect Detection Performance of PCB Inspection Based on ECT Technique With Multi-SV-GMR Sensor

Chomsuwan, Komkrit; Yamada, S.; Iwahara, M.

doi:10.1109/tmag.2007.893480

Cited by 22 publications

(13 citation statements)

References 6 publications

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“…Overall approach consists of two stages; detection of PCB defects and classification of defects. Chomsuwan et al [5] developed an automated approach for PCB defect detection. This approach is based on the analysis of testing of eddy current and magneto-resistive sensors.…”

Section: Introductionmentioning

confidence: 99%

PCB Defect Detection using Computer Vision based Symbolic Pattern Analysis Technique

Prathiba¹,

Nagendra²,

Hanumantappa³

2017

IJARCSSE

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Abstract-Recently manufacturing industries have experienced a significant improvement. In manufacturing industries, PCB (Printed Circuit Board) manufacturing is known as the most complex to maintain the quality by producing non-defective printed circuit boards for further process. In order to manufacture the printed circuit boards, various electronic components need to be manufactured which urge for manufacturing accuracy and quality assurance. In order to maintain the quality of these products, various schemes have been introduced which are based on the computer vision and data mining techniques. During PCB image acquisition, original image gets contaminated due to occlusions and orientation or camera quality etc. which degrades the manufacturing quality and leads to the defective product manufacture. To cater this issue, we develop a novel approach for defect detection using computer vision scheme where we apply various pre-processing techniques and classification technique. In this work, a new classifier is developed based on the image background, foreground and defect shadow analysis. This article aims on producing an automated process for PCB defect detection using computer vision application with lower complexities. In order to carry out this work, PCB image is transformed into symbols and various features are extracted from the image by dividing image into sub-regions i.e. background, foreground and shadow of defective region. Finally, a binary classifier is constructed with the help of symbolic dynamics to provide improved classification performance. Experimental study shows improved performance of proposed model when compared with existing state of art algorithms. Keywords-PCB defect; computer vision; symbolic pattern; finite automata; classification I. INTRODUCTIONBare printed circuit boards are the type of electronic components which are used for placing other electronic components for any electronic component manufacturing process [1]. PCB is an important part for producing any electronic component. Now a day, technology has grown rapidly which results in higher production of electronic components and devices. During this mass manufacturing because of various issues some electronic components are produced defective which are not acceptable by consumers or doesn't meet industry manufacturing criteria. These issues increase the cost of manufacturing because a manual inspection is required for better quality assurance. Since electronic components are produced in a massive number where manual inspection is not feasible to implement. To adapt up to this issue, different procedures have been produced as of late for robotized PCB imperfection discovery and characterization. Ercal et al. [2] talked about this issue of PCB imperfection recognition where three classes of PCB assessment methodologies are examined which are given as: (a) reference based strategy (b) non-reference based review system and (c) half and half procedure for PCB deformity investigation. Reference based assessment approaches uses correl...

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Section: Introductionmentioning

confidence: 99%

PCB Defect Detection using Computer Vision based Symbolic Pattern Analysis Technique

Prathiba¹,

Nagendra²,

Hanumantappa³

2017

IJARCSSE

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“…Along with a variety of methods that include ultrasonic testing, dye penetrants and X-ray, eddy current testing is also commonly used for detecting fatigue cracks in conductive materials such as aircraft carriers and jet engines (Dogaru, C. Smith, Schneider, & S. Smith, 2004;Grimberg, Udpa, Udpa, & Savin, 2005;Uesaka et al, 1995). Recently, high frequency eddy current testing has been developed to detect micro defects on micro conductors of bare PCBs with various types of pick up sensors (Chomsuwan, Yamada, & Iwahara, 2007a, 2007bChomsuwan, Yamada, Iwahara, Wakiwaka, & Shoji, 2005;Kacprzak, Taniguchi, Nakamura, Yamada, & Iwahara, 2001;Yamada et al, 2004;Yamada, Chomsuwan, Hagino, Tian, & Iwahara, 2005;Yamada, Chomsuwan, & Iwahara, 2006;Yamada, Nakamura, Iwahara, Taniguchi, & Wakiwaka, 2003). In this paper, a low frequency eddy current testing probe structure is proposed which consists of Helmholtz coils exciter and a pick-up sensor made of 5 turn coils in planar array.…”

Section: Introductionmentioning

confidence: 99%

Printed Circuit Board Fault Inspection Based on Eddy Current Testing Using Planar Coil Sensor

Soeung¹,

Ali²,

Khir³

et al. 2014

MAS

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This paper presents a printed circuit board (PCB) fault inspection method using eddy current testing generated from Helmholtz coils with a planar array-coil sensor to locate and inspect short and open faults on uniformly spaced interconnect single layer PCBs. The differences between the induced voltages from fault-free boards and faulty boards will be recorded in tables and translated into contour plots. The experimental results showed that in the presence of a short fault, the differences between the induced voltages from fault-free and faulty boards are highly negative. However, in the presence of an open fault, the differences between the induced voltages from fault free and faulty boards are highly positive. These highly positive or negative induced voltages can be translated into high density color regions on contour plots. The potential fault positions can be located by observing the color regions of the contour plots with respect to each element of the matrix sensor.

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“…M In ECNDT, one of the principal challenges is to determine the size of deep defects in the multi-layered structures based on information contained in the signal representing the change in impedance of the coil as it scans over the specimen. For single-layered structure materials, the high degree of accuracy of some numerical simulation techniques has been demonstrated and several fast computational methods have been presented [9][10][11]. However, the quantitative estimating size of deep defects in multi-layered structures still poses a major challenge and remains to be dealt with [12].…”

Section: Introductionmentioning

confidence: 99%

Quantitative estimating size of deep defects in multi-layered structures from eddy current NDT signals using improved ant colony algorithm

Ye¹,

Li²,

Qiu³

et al. 2014

Frattura Ed Integrità Strutturale

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Detection and quantitative estimation of deep defects in multi-layered structures is an essential task in a range of technological applications, such as maintaining the integrity of structures, enhancing the safety of aging aircraft, and assuring the quality of products. A novel approach to accurately quantify the two-dimensional axisymmetric deep defect size from eddy current nondestructive testing (NDT) signals is presented here. The method uses a finite element forward model to simulate the underlying physical process and an improved ant colony algorithm (IACA) to solve the inverse problem. Experiments are carried out. The performance comparison between the IACA method and the least square method is shown. The comparison results demonstrate the feasibility and validity of the IACA method. Between them, the IACA method gives a better estimation performance than the least square method at present.

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Improvement on Defect Detection Performance of PCB Inspection Based on ECT Technique With Multi-SV-GMR Sensor

Cited by 22 publications

References 6 publications

PCB Defect Detection using Computer Vision based Symbolic Pattern Analysis Technique

PCB Defect Detection using Computer Vision based Symbolic Pattern Analysis Technique

Printed Circuit Board Fault Inspection Based on Eddy Current Testing Using Planar Coil Sensor

Quantitative estimating size of deep defects in multi-layered structures from eddy current NDT signals using improved ant colony algorithm

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