Technical Library | 2023-07-22 02:26:05.0
Patch offset; Uneven patches throughout the substrate (each substrate is offset in a different way); Only part of the substrate is offset; Only certain components are offset; The patch Angle is offset; Component absorption error; Laser identification (component identification) error; Nozzle loading and unloading error; Mark (BOC mark, IC mark) identification error; Image recognition error (KE-2060 only); Analysis of the main reasons for throwing material. More information about KINGSUN please Contact US at jenny@ksunsmt.com or visit www.ksunsmt.com
Technical Library | 2021-05-06 13:48:05.0
In this paper most commonly occurring Bare PCB defects such as Track Cut, Track short and Pad Damages are detected by Image processing techniques. Reference PCB without having any defects is compared with test PCB having defects to identify the defects and x-y coordinates of the center of the defects along with radii are obtained using Difference of Gaussian method and location of the individual type of defects are marked either by similar color or different colors. Result Analysis includes time taken for the inspection of a single defect, multiple similar defects, and multiple different defects. Time taken is ranging from 1.674 to 1.714 seconds if the individual type of defects are marked by different colors and 0.670 to 0.709 seconds if all the identified defects are marked by the same colors.
Technical Library | 2024-04-29 21:19:42.0
Over the years, computer vision and machine learning 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"1, 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 PCBAVI, discusses challenges, describes the proposed dataset, and presents baseline performances using feature engineering and deep learning methods for PCB component classification.
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