PCB Error Detection Using Image Processing

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ISSN 2395-1621 PCB Error Detection Using Image Processing #1 Akshay Govind Lahane, #2 Anish Sanjay Dixit, #3 Pratik Surendrakumar Kadam, #4 Shripad Rajendra Angre 1 Akshayraje3137@gmail.

1 ISSN PCB Error Detection Using Image Processing #1 Akshay Govind Lahane, #2 Anish Sanjay Dixit, #3 Pratik Surendrakumar Kadam, #4 Shripad Rajendra Angre 1 Akshayraje3137@gmail.com 1 2 anishdixit@gmail.com 2 3 Pratikkadam8237@gmail.com 3 4 Shri.angre99@gmail.com 4 #1234 E&TC Department, PVPIT BAVDHAN, PUNE ABSTRACT Printed circuit boards are by far the most common method of assembling modern electronic circuits. During the manufacturing of PCB many defects are introduced which are harmful to precision circuit performance. A variety of ways has been established to detect the defects found on PCB, but it is also necessary to classify these defects so that the source of these defects can be identified. This study proposes an algorithm to group all 14 defects found on PCB into 5 groups. The proposed algorithm involves MALAB image processing operations, such as image subtraction, image addition, logical XOR, flood fill, Opening,erosion. Keywords PCB, Defects, Image processin, MATLAB, Opening ARTICLE INFO Article History Received :24 th May 2016 Received in revised form : 26 th May 2016 Accepted : 28 th May 2016 Published online : 31 st May 2016 I. INTRODUCTION A bare printed circuit board (PCB) is a PCB that is used before the placement of components and the soldering process [1]. It is used along with other components to produce electronic goods. During the manufacturing of printed circuit boards, widths of insulators and conductors can change because of manufacturing defects such as dust, overetching, underetching, and spurious metals. To reduce manufacturing costs associated with defected bare PCBs, the inspection of bare PCBs is required as the foremost step of the manufacturing process. The objective of printed circuit board (PCB) inspection is to verify that the characteristics of board manufacturing are in conformity with the design specifications. PCB defects can be categorized into two groups; functional defects and cosmetic defects [2]. Functional defects can seriously affect the performance of the PCB or cause it to fail. Cosmetic defects affect the appearance of the PCB, but can also jeopardize its performance in the long run due to abnormal heat dissipation and distribution of current. Three categories of PCB inspection algorithms have been proposed. In recent years, the PCB industries required automation due to many reasons. The most important one is the technological advances in PCBs design and manufacturing. New electronic component fabrication technologies require efficient PCB design and inspection method with compact dimension. Another important factor is necessity to reduce the inspection duration. These factors lead to automation in PCB industry. NowadayS automated systems are preferred in manufacturing industry for higher productivity. In this paper we can use a non-contact reference based, image processing approach for defects detection and classification. A template of adefect detection and a defected test PCB image are segmented and compared with each other procedures. II. PCB DEFECTS PCB defects can be categorized into two groups:- 1. Functional defects: functional defects can seriously affect the performance of the PCB or cause it to fail. 2. Cosmetic defects: cosmetic defects affect the appearance of the PCB, but can also jeopardize its performance in the long run due to abnormal heat dissipation and distribution of current. There are 14 known types of defects for single layer, bare PCBs as shown in table1. Various defects have been shown in figure 1 and , IERJ All Rights Reserved Page 1

III. METHODS OF FINDINGDEFECTS The various methods are used for finding the defects on PCB are follow. Bare PCB is one without any placement of electronic components to produce electric goods.

Moganti etal,[3] proposed three categories of PCB inspection algorithms referential approaches, non-referential approaches, and hybrid approaches. A.

2 III. METHODS OF FINDINGDEFECTS The various methods are used for finding the defects on PCB are follow. Bare PCB is one without any placement of electronic components to produce electric goods. In order to reduce cost spending in manufacturing caused by the defected bare PCB, it must be inspected. Moganti etal,[3] proposed three categories of PCB inspection algorithms referential approaches, non-referential approaches, and hybrid approaches. A. Automated Optical Inspection(AOI) Figure 1: Template image of bare PCB Automated optical inspection (AOI) is an automated visual inspection of PCB where a camera autonomously scans the device under test for both catastrophic failure and quality defects. It is commonly used in the manufacturing process due to the fact is a non-contact test method. It is implemented at many stages through the manufacturing process including bare board inspection, solder paste inspection (SPI), pre and post reflows as well as other stages AOI for a bare PCB board inspection may detect these features 1. Line width violations 2. Spacing violation 3. Excess copper 4. Short circuits 5. Cuts 6. Hole breakage Figure 2: Defective image of bare PCB 1. Breakout 2. Pin hole 3. Open circuit 4.Underetch 5.Mouse bite 6.Missing conductor 7.Spur 8 Short 9.Wrong size hole 10.Conductor too close 11.Spuriouscooper 12.Excessive short 13.Missing hole 14.Over etch Table 1: Various defects on bare PCB B. Image Subtraction Method We first compare a PCB standard image with a PCB image, using a simple subtraction algorithm that can highlight the main problem-regions. We have also seen the effect of noise in a PCB image that at what level this method is suitable to detect the faulty image. Our focus is to detect defects on printed circuit board to see the effect of noise. Nowadays is necessary to improve the quality of PCB Image subtraction operation is performed in order to get the differences between two images. Fig represents the block diagram of image subtraction. The subtraction operation will produce either negative or positive image, 1 represents white pixel and 0 represents black pixel in a binary image Tworules exists for image subtraction operation Rule 1: If 1-0=1 then it gives positive pixel image Rule 2: If 0-1=-1 then it gives negative pixel image For the image subtraction operation it is required that both images has same size in terms of pixels. The logical XOR operation gives defects in inspected image as compared with reference image 2015, IERJ All Rights Reserved Page 2

3 BLOCK DIAGRAM DESCRIPTION IV. PROPOSED SYSTEM In the previous section we discussed various methods of defect detection. The proposed is detection of defects on PCB using some of image processing technique. This project aims to propose in detecting and classifying the defects on bare single layer PCBs by introducing a hybrid algorithm. This project proposes a PCB defect detection and classification system using a morphological image segmentation algorithm and image processing theories. This project plans to use template and test images of single layer, bare computer generated PCBs. This project uses mathematical morphology for image segmentation, and image processing algorithm for detection and classification of PCB defects. V. BLOCK DIAGRAM Figure3. Block Diagram of Error Detection System Using Image Processing Rolling Platform: This platform will be made with the help of a dc motor which will be control by microcontroller. DC motor will be control by motor driver IC L293D. Length of the platform will be around 4 feet so that at least four PCBs can be placed on it. Microcontroller: For our system we are using AVR ATmega16 microcontroller. It will control the movement of the rolling platform and will notify the testing status by sending data to LCD. Also it will handle the communication through laptop and smart phone. Basically AVR microcontroller will act as a central controlling unit. LCD Display A 16x2 LCD display will be used for notifying the status of the PCB testing. If no error found then it will display Tested OK and in case of some fault in PCB ERROR message will be displayed. Wi-Fi Module To make our system easier to handle we will be providing a mobile app so that user can control operation like On & Off from distant using smartphone. Also user can receive the status of testing like error on phone. For this system we are going to use EPS8266 Wi-Fi module. Power Supply It requires 5VDC power supply to operate DC motor and microcontroller. Also 3.3VDC is required to operate EPS8266 Wi-Fi module. So as per our need we will be designing a 5V and 3.3V DC power supply form 230VAC. A step-down transformer is used to get 12V AC which is later converted to 12V DC using a rectifier. The output of rectifier still contains some ripples even though it is a DC signal due to which it is called as Pulsating DC. To remove the ripples and obtain smoothed DC power filter circuits are used. Here a capacitor is used. The 12V DC is rated down to 5V using a positive voltage regulator chip Thus a fixed DC voltage of 5V is obtained. Then regulator LM 317 will be used to convert 5V to 3.3 VDC. VI. SCOPE OF STUDY There is always a further scope of study for every system even if it is smooth in performance. The study is classified into 5 phases: 1. Idea studies 2. Feasibility study- The technical work in this phase should focus on new and modified equipments in systems that obviously will be affected by the actual design requirements. 3. Concept study- The objective of the concept phase is to select and define the modification concept for realizing a business opportunity, reduce operational expense, and demonstrate that execution risk is satisfactory to the company requirements and business plans. 4. Detail Engineering- To achieve the required goal of accuracy the disciplines involved must be well coordinated. 2015, IERJ All Rights Reserved Page 3

5. Pre-Engineering- The technical documentation to be further matured defining basis for project execution.

Machine vision may answer the manufacturing industry s need to improve product quality and increase

The major limitation of all the existing inspection system is that all the algorithms need a special

4 5. Pre-Engineering- The technical documentation to be further matured defining basis for project execution. VII. CONCLUSION Various advances took place in PCB manufacturing industry over the last decade. Machine vision may answer the manufacturing industry s need to improve product quality and increase productivity. This study presented a survey of algorithm for visual inspection of PCB. The major limitation of all the existing inspection system is that all the algorithms need a special hardware platform in order to achieve the desired real-time speeds, which make the systems extremely expensive. Integration with an image capturing systems such as camera, frame grabber and personal computer is also essential for actual performance verification of defect detection and classification of PCBs. VIII. RESULT 2015, IERJ All Rights Reserved Page 4

5 REFERENCES [1] J. Hong, K. Park and K. Kim, Parallel processing machine vision system for bare PCB inspection, Proc. of the 24th Annual Conference of the IEEE, pp , [2] Ismail Ibrahim, Zuwairie Ibrahim, Kamal Khalil, Musa Mohd Mokji Syed Abdul Rahman Syed Abu Bakar, Norrima Mokhtar and Wan Khairunizam Wan Ahmad, An improved defect classification algorithm for six Printing defects and its implementation on real Printed circuit board images, International Journal of Innovative Computing, Volume 8, Number 5(A), May 2012 [3]M. Moganti, F. Ercal, C. H. Dagli and S. Tsunekawa, automatic PCB inspection algorithms: A survey compter vision and image understanding year =1996, volume= , IERJ All Rights Reserved Page 5

Defect detection and classification of printed circuit board using MATLAB

Defect detection and classification of printed circuit board using MATLAB Mr.M.H.Thigale 1, Shivani Gaikwad 2, Priyanka Nangare 3, Nivedita Hule 4 1Mr.M.H.Thigale, Assistant Professor, Dr. D Y Patil Institute