International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
www.irjet.net
Defect detection and classification of printed circuit board using MATLAB Mr.M.H.Thigale1, Shivani Gaikwad2, Priyanka Nangare3 , Nivedita Hule4 1Mr.M.H.Thigale,
Assistant Professor, Dr. D Y Patil Institute Of Engineering Management Research, Akurdi. Gaikwad, Dr. D Y Patil Institute Of Engineering Management Research, Akurdi. 3Priyanka Nangare, Dr. D Y Patil Institute Of Engineering Management Research, Akurdi. 4Nivedita Hule, Dr. D Y Patil Institute Of Engineering Management Research, Akurdi. ---------------------------------------------------------------------***--------------------------------------------------------------------Nowadays various algorithms are developed for PCB Abstract - The importance of the Printed Circuit Board defect detection some of them are Referential, Non inspection process has been magnified by requirements of the modern manufacturing environment. In electronics mass referential, Hybrid , Contact Method And Noncontact production manufacturing facilities, an attempt is often to Method. In this project, Defect detection and achieve 100% quality assurance. In this work Machine Vision classification is done using image processing approach PCB Inspection System is applied at the first step of which is part of noncontact algorithm .Here we also manufacturing. In this system a PCB inspection system is used normalized cross correlation which differentiate proposed and the inspection algorithm mainly focuses on the between defective and defect free PCB . Depending on defect detection and defect classification of the defects. Defect NCC result , if pcb is defected further segmentation is classification is essential to the identification of the defect done on pcb .After segmentation ,by using arithmetic sources. The purpose of the system is to provide the automatic and image processing operation we can detect defects defect detection of PCB and relieve the human inspectors from and classify based on similarities and area of the tedious task of finding the defects in PCB which may lead to electric failure. We first compare a standard PCB inspection occurance. There are 14 known types of defects for image with a PCB image to be inspected. Normalized Crosssingle layer, bare PCBs as shown in Table I. 2Shivani
Correlation has been used to differentiate between defective and defect free printed circuit board. Different PCBs have been inspected using normalized cross-correlation and further defected PCBs have been used for detection of all possible defects. Here we proposes a PCB defect detection and classification system using a morphological image segmentation algorithm and simple the image processing theories. The proposed algorithm group all 14 defects found on PCB into 7 Groups .The proposed algorithm involves MATLAB image processing operations such as image subtraction, logical XOR, Flood fill. Key Words: Printed circuit board, Normalized cross correlation, Morphological segmentation, Image processing, Defect detection, Defect classification.
1. INTRODUCTION Visual inspection is one of the largest cost consuming process in PCB .It also responsible for detecting various types of defects and ensure quality assurance for all finished product. There are two process included in pcb inspection. -Defect Detection -Defect Classification. Š 2017, IRJET
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Impact Factor value: 5.181
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2. LITERATURE SURVEY Moganti proposed firstly three categories of PCB inspection algorithms: 1]referential approach: where comparision is made between test and reference image. 2] non-referential approaches :where general design rule verify such as width of conductor and insulator 3] hybrid approaches: where combination of Referential approach and Non-referential approaches ISO 9001:2008 Certified Journal
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