sir im anand . im looking for the matlab code . we followed a specific algorithm and we were not successful in getting our desired output. we kindly request you to guide us with the coding part

mathlab code for pcb defect detection system

pcb defect detection matlab code

This project presents a new implementation which separates two of the existing groups containing two defects each into four new groups containing one defect each by processing synthetic images of bare through-hole single layer PCBs using MATLAB Image Processing. This also will use Mathematical Morphological Toolbox here. Morphological process involves techniques such as dilation, erosion, opening and closing which helps in partitioning the images and associates certain types of defects with certain patterns. This project separates the defects in larger groups into smaller groups. This increases the efficiency of the inspection system in classifying defects. Since certain PCB pattern are produced in different processes, classification of defects can help in determining the root causes of error and reduce production cost in the long run. Focus is given to separate groups of defects in the hole segment, thick line segment and thin line segment. An automated visual printed circuit board (PCB) inspection is an approach used to counter difficulties occurred in human’s manual inspection that can eliminates subjective aspects and then provides fast, quantitative, and dimensional assessments. Various concentrated work on detection of defects on printed circuit boards (PCBs) have been done, but it is also crucial to classify these defects in order to analyze and identify the root causes of the defects. This project proposes a PCB defect detection and classification system using a morphological image segmentation algorithm and simple the image processing theories. However, besides the need to detect the defects, it is also essential to classify and locate these defects so that the source and location of these defects can be identified. Based on initial studies, some PCB defects can only exist in certain groups. Thus, it is obvious that the image processing algorithm could be improved by applying a segmentation exercise. This project uses template and test images of single layer, bare, grayscale computer generated PCBs. The importance of the Printed Circuit Board inspection process has been magnified by requirements of the modern manufacturing environment. In electronics mass production manufacturing facilities, an attempt is often to achieve 100% quality assurance. In this work Machine Vision PCB Inspection System is applied at the first step of manufacturing. In this system a PCB inspection system is proposed and the inspection algorithm mainly focuses on the defect detection and defect classification of the defects. Defect classification is essential to the identification of the defect sources. The purpose of the system is to provide the automatic defect detection of PCB and relieve the human inspectors from the tedious task of finding the defects in PCB which may lead to electric failure. We first compare a standard PCB inspection image with a PCB image to be inspected. The MATLAB tool is used to detect the defects and to classify the defects. With the solutions designed and implemented in this thesis the algorithm designed in the proposed system is able to detect and classify all the known 14 types of defects successfully with greater accuracy. The algorithm makes use of image subtraction method for defect detection and kNN classification algorithm for the classification of the defects. This thesis will present and analyze the performance of the proposed inspection algorithm. The experiment will measure the accuracy of the system.