Abstract
Automatic inspection based on a real-time machine vision system may serve as substitute for the manual human visual inspection of flux defects in Printed Circuit Boards (PCBs), which often cause damage on the board in the form of corrosions that harm the assembly. The concept of automatic inspection contributes to the improvement of the manufacturing quality of PCBs and facilitates their approval or rejection. The Automatic Inspection System for Printed Circuit Boards (AIS-PCB) is developed with the capability to identify the defects and the quality of PCBs. It is based on a real time system machine vision. The developed AIS-PCB is capable of detecting, indexing and classifying by measuring the flux defects in PCBs during the re-flow of the real-time process. The AIS-PCB is The total automation control system is the core of the AIS-PCB. This system consist of vision inspection station, mechanical loader and unloader, final decision station and the pneumatic system handler. To detect and classify the quality of PCBs, segmentation in conjunction with Radon transform approaches are used for feature indexing and line detection based on the gradient field of PCB images. The Feed-Forward Back-Propagation (FFBP) model is used to classify the product quality of the PCBs via a learning concept. A number of trainings using the FFBP are performed to learn and match the targets. The images of each PCB classes are used as inputs to the classification module. The obtained results from the classification and rule decision are used to establish the receiver operating characteristic curve. The classifier, which is based on the proposed approach and is tested on the PCBs from a factory’s production line, achieves a sorting Coefficient Of Efficiency (COE >95%). The developed AIS-PCB system shows promising results in successfully segmenting and classifying flux defects in PCBs through computerized visual information and facilitates their automatic inspection, thereby aiding humans in conducting rapid inspections.
Highlights
MotivationComputer vision techniques are used for the computerized visual inspection of Printed Computer Boards (PCBs), for the assessment of their various defects
The specific objectives of this study are the following: (1) To extract the PCB images flux as the Region Of Interest (ROI) through flux segmentation approach, (2) to index and extract features from the resulting segmented images and (3) to produce a robust model based on an Artificial Neural Network (ANN) classifier that can distinguish between PCBs with flux defects and those without at a high classification accuracy
Brown and Davis (2006) the results identified is linked with the estimation of the sensitivity and specificity of the Receiver Operating Characteristic (ROC) curve set up
Summary
Computer vision techniques are used for the computerized visual inspection of Printed Computer Boards (PCBs), for the assessment of their various defects. The fault detection strategy applies referential inspections methods whereby the manufactured or an artwork board serves as the references without any errors. Defects of PCB can be divided into two classes: Rejected units referred to as fatal defects and accepted. Extensive labor works and unpredictable grading are common while manually inspecting the PCB defects. This attributes makes it disadvantageous to use. For example in printing, markings of components, disoriented components, labelling and such can be recognized by the system
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