Automatic PCB Inspection Systems

There are more than 50 process steps required to fabricate a printed circuit board (PCB). To ensure quality, human operators simply inspect the work visually against prescribed standards. The decisions made by this labor intensive, and therefore costly, procedure often also involve subjective judgements. Automatic inspection systems remove the subjective aspects and provide fast, quantitative dimensional assessments. Machine vision may answer the manufacturing industry\u27s need to improve product quality and increase productivity. The major limitation of existing inspection systems is that all the algorithms need a special hardware platform to achieve the desired real-time speeds. This makes the systems extremely expensive. Any improvements in speeding up the computation process algorithmically could reduce the cost of these systems drastically. However, they remain a better option than increasingly error prone, and slow manual human inspectio

Bare PCB Inspection System With SV-GMR Sensor Eddy-Current Testing Probe

This paper describes bare printed circuit board (PCB) inspection based on eddy-current testing (ECT) technique with high scanning speed. A high-frequency ECT probe composed of a meander coil as an exciting coil and the spin-valve giant magnetoresistance (SV-GMR) sensor was fabricated and is proposed. The ECT probe was designed based on crack inspection over flat surface, especially suitable for microdefect detection on high-density bare PCB. The ECT signal detected by the SV-GMR sensor was acquired by high-speed A/D converter for applying the signal processing based on digital technique. Harmonic analysis based on Fourier transform was used to analyze the ECT signal at fundamental frequency in order to increase inspection speed and this technique allowed the ECT probe to scan bare PCB, with high sampling frequency and with high-spatial resolution inspection. Experimental results verified the possibility and the performance of the proposed PCB inspection system based on ECT technique. © 2007 IEEE.Power Electronics and Drive Systems, 1997. Proceedings., 1997 International Conference o

Bare PCB inspection system with SV-GMR sensor eddy-current testing probe

金沢大学環日本海域環境研究センター生体機能計測研究部門This paper describes bare printed circuit board (PCB) inspection based on eddy-current testing (ECT) technique with high scanning speed. A high-frequency ECT probe composed of a meander coil as an exciting coil and the spin-valve giant magnetoresistance (SV-GMR) sensor was fabricated and is proposed. The ECT probe was designed based on crack inspection over flat surface, especially suitable for microdefect detection on high-density bare PCB. The ECT signal detected by the SV-GMR sensor was acquired by high-speed A/D converter for applying the signal processing based on digital technique. Harmonic analysis based on Fourier transform was used to analyze the ECT signal at fundamental frequency in order to increase inspection speed and this technique allowed the ECT probe to scan bare PCB, with high sampling frequency and with high-spatial resolution inspection. Experimental results verified the possibility and the performance of the proposed PCB inspection system based on ECT technique. © 2007 IEEE

FICS-PCB: A Multi-Modal Image Dataset for Automated Printed Circuit Board Visual Inspection

Over the years, computer vision and machine learn- ing disciplines have considerably advanced the field of automated visual inspection for Printed Circuit Board (PCB-AVI) assurance. However, in practice, the capabilities and limitations of these advancements remain unknown because there are few publicly accessible datasets for PCB visual inspection and even fewer that contain images that simulate realistic application scenarios. To address this need, we propose a publicly available dataset, “FICS-PCB”, to facilitate the development of robust methods for PCB-AVI. The proposed dataset includes challenging cases from three variable aspects: illumination, image scale, and image sensor. This dataset consists of 9,912 images of 31 PCB samples and contains 77,347 annotated components. This paper reviews the existing datasets and methodologies used for PCB- AVI, discusses challenges, describes the proposed dataset, and presents baseline performances using feature engineering and deep learning methods for PCB component classification

Three-dimensional scene recovery for measuring sighting distances of rail track assets from monocular forward facing videos

Rail track asset sighting distance must be checked regularly to ensure the continued and safe operation of rolling stock. Methods currently used to check asset line-of-sight involve manual labour or laser systems. Video cameras and computer vision techniques provide one possible route for cheaper, automated systems. Three categories of computer vision method are identified for possible application: two-dimensional object recognition, two-dimensional object tracking and three-dimensional scene recovery. However, presented experimentation shows recognition and tracking methods produce less accurate asset line-of-sight results for increasing asset-camera distance. Regarding three-dimensional scene recovery, evidence is presented suggesting a relationship between image feature and recovered scene information. A novel framework which learns these relationships is proposed. Learnt relationships from recovered image features probabilistically limit the search space of future features, improving efficiency. This framework is applied to several scene recovery methods and is shown (on average) to decrease computation by two-thirds for a possible, small decrease in accuracy of recovered scenes. Asset line-of-sight results computed from recovered three-dimensional terrain data are shown to be more accurate than two-dimensional methods, not effected by increasing asset-camera distance. Finally, the analysis of terrain in terms of effect on asset line-of-sight is considered. Terrain elements, segmented using semantic information, are ranked with a metric combining a minimum line-of-sight blocking distance and the growth required to achieve this minimum distance. Since this ranking measure is relative, it is shown how an approximation of the terrain data can be applied, decreasing computation time. Further efficiency increases are found by decomposing the problem into a set of two-dimensional problems and applying binary search techniques. The combination of the research elements presented in this thesis provide efficient methods for automatically analysing asset line-of-sight and the impact of the surrounding terrain, from captured monocular video.EThOS - Electronic Theses Online ServiceGBUnited Kingdo