A PRINTED CIRCUIT BOARD INSPECTION SYSTEM WITH DEFECT CLASSIFICATION CAPABILITY (original) (raw)

Automated inspection of printed circuit boards through machine vision

Computers in Industry, 1996

This paper introduces the development of an automated visual inspection system for printed circuit boards (PCBs). It utilizes an elimination-subtraction method which directly subtracts the template image from the inspected image, and then conducts an elimination procedure to locate defects in the PCB. Each detected defect is subsequently classified into one of the seven defect types hy three indices: the type of object detected, the difference in object numbers, and the difference in background numbers between the inspected image and the template. Finally, a 256 X 240 PCB image was tested to show the effectiveness of this system.

2-Automated inspection of printed circuit boards through machine vision

Image Processing Based Defect Detection of Printed Circuit Board

Visual inspection is one of highest cost in printed circuit boards (PCB) manufacturing. Although many algorithms are available in defect detection, both contact and non-contact methods none is able to detect the defects accurately. Contact method tests the connectivity of circuits but unable to detect major flaws in cosmetic defects. Non-contact uses methods such as ultrasonic and x-ray imaging to detect anomalies in the circuit design. The use of manual labor to visually inspect each PCB is no longer viable since it is prone to human errors, time consuming, requires large overhead costs and results in high wastage. Thus an automation inspection system is highly desirable. This project aims at demonstrating real time PCB Defect Identification using digital image processing. This does not involve using a specialized sensors attached to the PCB. Printed circuit defects are mainly missing or extra elements on the board. PCB defects can be categorized into two groups; functional defects and cosmetic defects. Functional defects can be fatal to the circuit operations while cosmetic defects affect the appearance of the circuit board but may affect the performance of the circuit in long term. Index Terms—PCB, functional defects and cosmetic defects

PCB Fault Detection Using Image Processing

IOP Conference Series: Materials Science and Engineering, 2017

The importance of the Printed Circuit Board inspection process has been magnified by requirements of the modern manufacturing environment where delivery of 100% defect free PCBs is the expectation. To meet such expectations, identifying various defects and their types becomes the first step. In this PCB inspection system the inspection algorithm mainly focuses on the defect detection using the natural images. Many practical issues like tilt of the images, bad light conditions, height at which images are taken etc. are to be considered to ensure good quality of the image which can then be used for defect detection. Printed circuit board (PCB) fabrication is a multidisciplinary process, and etching is the most critical part in the PCB manufacturing process. The main objective of Etching process is to remove the exposed unwanted copper other than the required circuit pattern. In order to minimize scrap caused by the wrongly etched PCB panel, inspection has to be done in ear7ly stage. However, all of the inspections are done after the etching process where any defective PCB found is no longer useful and is simply thrown away. Since etching process costs 0% of the entire PCB fabrication, it is uneconomical to simply discard the defective PCBs. In this paper a method to identify the defects in natural PCB images and associated practical issues are addressed using Software tools and some of the major types of single layer PCB defects are Pattern Cut, Pin hole, Pattern Short, Nick etc., Therefore the defects should be identified before the etching process so that the PCB would be reprocessed. In the present approach expected to improve the efficiency of the system in detecting the defects even in low quality images

IJERT-A Survey : Automated Visual PCB Inspection Algorithm

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/a-survey-automated-visual-pcb-inspection-algorithm https://www.ijert.org/research/a-survey-automated-visual-pcb-inspection-algorithm-IJERTV3IS10032.pdf An automated visual printed circuit board (PCB) inspection is an approach used to counter difficulties occurred in human's manual inspection that can eliminate subjective aspects and then provides fast, quantitative, and dimensional assessments. A printed circuit board (PCB) is a basic component of many electronic devices. The quality of PCBs will have a significant effect on the performance of many electronic products. Presently, there has been a lot of work concentrating on the detection and classification of defects on PCB. There are so many approaches for automated visual inspection of printed circuits have been reported over the last two decades. In this survey the various algorithms and techniques are examined. A summary of commercial PCB inspection system is also presented.

An algorithm to group defects on printed circuit board for automated visual inspection

2008

Due to disadvantages in manual inspection, an automated visual inspection system is needed to eliminate subjective aspects and provides fast and quantitative assessment of printed circuit board (PCB). Up to the present, there has been a lot of work and research concentrated on PCB defect detection. PCB defects detection is necessary for verification of the characteristics of PCB to make sure it is in conformity with the design specifications. However, besides the need to detect the defects, it is also essential to classify these defects so that the source of these defects can be identified. Unfortunately, this area has been neglected and not been given enough attention. Hence, this study proposes an algorithm to group the defects found on bare PCB. Using a synthetically generated PCB image, the algorithm is able to group 14 commonly known PCB defects into five groups. The proposed algorithm includes several image processing operations such as image subtraction, image adding, logical XOR and NOT, and flood fill operator.

Automated Inspection System for Assembled Printed Circuit Board Using Machine Vision

Soft Computing Research Society eBooks, 2023

The perfect Printed Circuit Board (PCB) plays a very important role in every electronic device as well as in automation systems. So, it is very important to find defects in the PCB before installing it to any system or any device. However, PCB Manufacturers use various inspection systems in the process of manufacturing PCBs for detecting various types of defects in the PCB. In this article, we present the Automated assembled PCB Inspection System. This system finds defects such as missing components and improper position of its components by using the Pattern matching Technique where a good known score of template image is matched with the score of the test image. This system gives results at each inspection within 10 Seconds and the result given by this system are passed or fail in the form of an array sheet. This automated inspection system is created by using NI Vision Builder AI and NI LabVIEW technology. Ni Vision Builder AI has been used to create the algorithm. And NI LabVIEW has been used to create the application.

Defect Detection in Printed Board Circuit using Image Processing

International Journal of Innovative Technology and Exploring Engineering, 2019

A printed circuit board without connecting with any components called as a bare PCB. Consider a PCB as a basic part which has been settled with more electronic units. In order to display the manufacturing process, the drawbacks have been taken by PCB individually. The reflection of this separation process impacts the performance of the circuits. Also, we have examined about classification methodologies as well as referential based PCB detection. From the input images, the needed and related information has been pulled out using image processing methodologies by the referential based PCB detection. Comparing with the un-defected PCB images, this was used to find out the defects. To meet the goal of the PCB defect detection, several feature extraction and pre-processing methods are derived in this article. The PCB defects have been classified by those features using the machine learning algorithms. Moreover, several types of machine learning algorithms are derived in this article. Thi...

Automatic O ptical Inspection f or PCB M anufacturing : a Survey Eid M. Taha , E. Emary , Khalid Moustafa

As the rapid development in electronic industries based on Printed Circuit Board (PCB) designs and high volumes manufacturing capacities and the need for high quality products with minimum defect rate comes the importance of Automated Optical Inspection (AOI) technology. The basic objectives among different AOI system manufacturers are to improve lighting, computing capability, flexibility of part staging and vision software. These improvements make AOI products more intelligent, flexible, and with far more repeatable results that are superior to human visual inspection. For finding of errors in PCB many algorithms are proposed in literature. A variety of approaches for automated visual inspection of printed circuit have been reported over the last three decades. The last reported survey in this topic introduced by Moganti96 that is why the need to introduce this survey to cover reported work after 1996. Also Moganti survey covers solely bare PCBs visual inspection. In this survey, algorithms and techniques for the automated inspection of printed circuit boards are examined. A classification of these algorithms is presented and the algorithms are grouped according to this classification. This survey concentrates mainly on image analysis and fault detection techniques; these also include state-of-the-art algorithms.

Detection and Classification of Printed Circuit Boards Defects

Open Transactions on Information Processing, 2014

This paper presents inspection of Printed Circuit Boards (PCBs) based on normalized crosscorrelation. Correlation gives the similarity measure of images. In this paper, Normalized Cross-Correlation has been used to differentiate between a defective and defect free printed circuit board. Different PCBs have been inspected using normalized cross-correlation in this paper and further defected PCBs have been used for detection of all possible defects. Image subtraction(one of referential methods) is used for detection of defects of PCBs. And after detection of defects using image subtraction method, the wrong hole defects, missing conductors defects, etching defects, break lines have been classified.

A PRINTED CIRCUIT BOARD INSPECTION SYSTEM WITH DEFECT CLASSIFICATION CAPABILITY (original) (raw)

Related papers