Fuzzy Based Defect Detection in Printed Circuit Boards

Transcription

1 Volume 1, Number 1, October 2014 SOP TRANSACTIONS ON SIGNAL PROCESSING Fuzzy Based Defect Detection in Printed Circuit Boards Neha koul *, Gurmeet kaur, Beant kaur Department of Electronics And Communication Engineering, Punjabi University, Patiala, India PO Box *Corresponding author: Abstract: Printed Circuit Board (PCB) consists of circuit with electronic components mounted on its surface. There are three main steps involved in manufacturing of PCB and the inspection of PCB is necessary to reduce the defects. First step is printing, second step is the components fabrication over the PCB surface and third is the soldering of components. In this paper we have presented a PCB Inspection System which involves the use of correlation coefficient, image subtraction and fuzzy logic and the inspection system is applied at the first step of manufacturing, i.e., the making of bare PCB. The inspection system will not only show where the defects are present but will also show the degree of defectiveness present in the PCB. Typical defects that can be detected are over etchings (opens), under-etchings (shorts), holes etc. Keywords: Image Subtraction; Correlation Coefficient; Fuzzy Logic; Membership Functions 1. INTRODUCTION During the manufacturing of PCB there are some defects commonly found on PCB. These defects are broadly classified into two categories, potential and fatal defects. Short-circuit and open-circuit defects are in fatal defects category. Breakout, under etch, missing hole, and wrong size hole fall in potential defects category. Fatal defects are those defects in which the PCB does not meet the objective for which it is designed, while the potential defects are those which compromise the PCB performance during utilization [1]. During etching process, the anomalies occurring on bare PCB could be largely classified in two categories: the one is excessive copper and missing copper. The incomplete etching process leaves unwanted conductive materials and forms defects like Short-circuit, extra hole, protrusion, island and small space. Excessive etching leads to open circuit and thin pattern on PCB [2]. In addition, some other defects may exist on bare PCB, i.e. missing holes, scratch, and cracks [3]. In our inspection system, we will first subtract the image to be inspected with the reference image.the difference will detect the areas where the defect is present. After knowing the areas of defect we will find the correlation coefficient of the image under inspection with the standard image [4]. Then the value of the correlation coefficient will be given to the fuzzy system whose output will tell the degree of defectiveness in the printed circuit board [5]. 18

2 Fuzzy Based Defect Detection in Printed Circuit Boards 2. METHODOLOGY For the inspection of the printed circuit boards we first need to have a reference image which is absolutely defect free that is without any defect. Then we will compare the image we need to inspect with the reference image. Figure 1 and Figure 2 show the examples of reference PCB image and test PCB image. The defective image has a missing joint which is now to be detected [6]. Figure 1. Reference Image Figure 2. Test Image 2.1 Inspection Flow Chart In our inspection system, we will first subtract the image to be inspected with the reference image.the difference will detect the areas where the defect is present. After knowing the areas of defect we will find the correlation coefficient of the image under inspection with the standard image. Then the value of the correlation coefficient will be given to the fuzzy system whose output will tell the degree of defectiveness in the system. 2.2 Image Subtraction The image which is to be inspected will be subtracted from the reference image.after subtraction we will get the areas where the image is defected. If there is no defect then the difference will be zero otherwise the difference will have some value. In this paper we have used the difference operator to calculate the difference between the two images. Otherwise xor operator can also be used [7]. 19

3 Figure 3. Inspection Flow Chart Figure 4. Subtraction Result 20

4 Fuzzy Based Defect Detection in Printed Circuit Boards 2.3 Calculation of Correlation Coefficient So in subtraction result we came to know about the defect.the next step in the inspection system will be the calculation of the correlation coefficient. The value of the correlation coefficient will give the amount of similarity between the inspected image and the standard image [8]. This value of correlation will be given input to the fuzzy system on the basis of which we will calculate the degree of extent of defectiveness. 2.4 Design And Development Of Fuzzy Expert System A fuzzy expert system is a collection of membership functions and rules that are used to get the results. The part of the rule between the if and then is the rule s premise or antecedent. This is a fuzzy logic expression that explains the degree of applicability of the rule. This design has been divided into several steps. Steps are fuzzification, evaluation of rules and finally defuzzification. To design the system, the FIS tool in MATLAB R2013a is used. Fuzzy logic deals with the classes of objects that don t have sharp boundaries. It is used to get the output of a system whose input is vague. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In this study, the analysis focused on how to design an expert system to find the extent of defectiveness present in the PCB. First, the linguistic values and corresponding membership functions have been determined. Samples of values and corresponding membership functions for the input difference and correlation coefficient are shown in Figure 5 and Figure 6 respectively. Figure 7 shows the membership function and linguistic variables for the output PCB defect. Figure 5. Linguistic variable and membership function of Correlation Coefficient. 2.5 MATLAB 2013a The m file program in the MATLAB will be given the output of the fuzzy system then the program will give the extent of defectiveness present in the PCB based on the output of the fuzzy rule base. 21

5 Figure 6. Linguistic variable and membership function of Difference. Figure 7. Linguistic variable and membership function of PCB Defect. 3. RESULTS Fuzzy expert system is used to determine degree of defectiveness present in the PCB. This design consists of 2 inputs and 1 output. The inputs consist of difference and correlation coefficient while the output is the extent of defectiveness present in the PCB. The variables are used like low, medium and high for input and low, medium and high for output. The outline of our proposed fuzzy expert system can be shown in Figure 8. Mamdani method is used for fuzzification. Rule base is shown in Figure 9. Seven rules are used in this system. The rules have been developed using if-then method. Using these rules, the result risk in term of percentage (%) has been computed. Figure 10 shows the result for the defectiveness of the PCB.For the surface result; we can see the output for defect in PCB against two parameters i.e. difference and correlation coefficient. 4. CONCLUSION The bare PCB is analyzed and the defects of PCB are extracted. Due to the use of correlation coefficient the accuracy of the system is high. By the use of the above inspection method we not only get the areas where the defect is present but also the extent of defectiveness in the PCB is calculated. 22

6 Fuzzy Based Defect Detection in Printed Circuit Boards Figure 8. Fuzzy Expert System Figure 9. Rule Base Figure 10. The ruler view of proposed fuzzy expert system. 23

7 Figure 11. Surface view of proposed fuzzy expert system. 5. FUTURE WORK In order to use this method in an industrial application some improvements need to be done. Future work consists of inspecting and analyzing a PCB with Surface Mounted Devices. References [1] D.-B. Perng, C.-P. Liu, Y.-C. Chen, and C.-C. Chou, Advanced SMD PCB vision inspection machine development. 15th IPPR Conf Comput Vis Graph Image Process, [2] Y. Lu, Machine Vision Algorithms Using Interactive Learning for VFD Inspection, submitted to Journal of Applied Intelligence, [3] Y.-N. Sun and C.-T. Tsai, A new model-based approach for industrial visual inspection, Pattern Recognition, vol. 25, no. 11, pp , [4] W.-Y. Wu, M.-J. J. Wang, and C.-M. Liu, Automated inspection of printed circuit boards through machine vision, Computers in Industry, vol. 28, no. 2, pp , [5] F. R. Leta, F. F. Feliciano, and F. P. Martins, Computer vision system for printed circuit board inspection, [6] I. Ibrahim, Z. Ibrahim, M. S. Zainal Abidin, S. A. Bakar, S. A. Rahman, S. Sudin, et al., An algorithm for classification of six types of defect on bare printed circuit board, [7] K. Sundaraj, PCB inspection for missing or misaligned components using background subtraction, WSEAS Transactions on Information Science and Applications, vol. 6, no. 5, pp ,