Detection and Classification of Printed Circuit Boards Defects

Transcription

1 OPEN TRANSACTIONS ON INFORMATION PROCESSING Volume 1, Number 1, MAY 2014 OPEN TRANSACTIONS ON INFORMATION PROCESSING Detection and Classification of Printed Circuit Boards Defects B. Kaur*, G. Kaur and A. Kaur Department of Electronics & Communication Engineering, Punjabi University, India. *Corresponding author: Abstract: 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. Keywords: Defects; Image subtraction method; Normalized Cross-Correlation; Printed Circuit Boards 1. INTRODUCTION Inspection of Printed Circuit Board(PCB) is one of the important process since in the manufacturing processes there are uncertainties like tolerances, accurate position and sometimes orientation errors. The inspection system can inspect the printed circuit boards on many levels. Bare printed circuit board is a PCB without any electronic components placed on it. In order to reduce the cost which is spent on manufacturing caused by the defected bare PCB, the bare PCB must be inspected [1].Different methods have been developed for the computer based inspection of the PCBs. These methods can be classified as referential and non-referential. In referential method, test image has to be compared with the base image, which is considered to be perfect image. Examples of referential methods are image subtraction method [1], dimension verification and expansion/contraction [2], template matching [3]. The main advantage of the normalized cross correlation is that it is less sensitive to linear changes in the amplitude of illumination in the base image and test image [4]. In this paper, Normalized cross- correlation is used to differentiate between a defective and defect free PCB and furtherimage subtraction method is used for detection of present defects in defective PCBs as well as for classification of defects. Normalized cross-correlation helps to make the detection and classification more time efficient as detection and classification is only applied on defective PCBs. 8

2 Detection and Classification of Printed Circuit Boards Defects Table 1. TRUTH TABLE FOR IMAGE SUBTRACTION Bit 1 Bit2 Output NORMALIZED CROSS-CORRELATION (NCC) Normalized cross-correlation has been used for detection of defective PCBs in this paper. The Normalized Cross- Correlation gets the values between 0 & 1. If it is equal to 0 it means no correlation and if it is equal to 1 it means image is matched with each other. This approach has been used to detect the defects on the PCBs.When the test image and base image is same, means test image has no defect, then NCC gets the value 1 [5]. NCC is a powerfulsimilarity measure in computer vision, which has been used for applications like object recognition and inspection of defects [6]. In this paper the application of inspection of defects has been presented. Mathematical equation for NCC is as under [5]: NCC = where X i is base image and Y i is the test image. MN i=1 X iy i MN i=1 X i 2 MN i=1 Y i 2 (1) 3. IMAGESUBTRATION METHOD Image subtraction is a kind of pixel subtraction process, where by the numeric value of one pixel or the complete image is subtracted from the image. The image subtraction operator takes two images as input and third image as output, whose pixel values are simply those of the first image minus the corresponding pixel values from the second image.image subtraction method can be implemented on assembled PCBs [7], solder pasted PCBs [8]. Solder joint inspection based on neural network combined with genetic algorithm had also been proposed [9]. Authors had earlier implemented the image subtraction method to obtain the differences between the two images and classification of different defects [10]. For the classification of the defects, the resultant difference image has been used [11].Image subtraction method can be combined with wavelets to reduce pixel value of image to detect the defects [12, 13]. The method had compared both the images pixel-by-pixel using XOR logic operator. The resulting image had shown only the defects. Image subtraction can be done by using equation as under [14]: Z(i, j) = X(i, j) Y (i, j) (2) where, Z is the output image, X is the reference input image, Y is the inspected input image, represents exclusive OR operation. In Table 1, it has been shown that if the bit1 (pixel value) of base image is not equal to pixel value of test image (bit 2) then there is a defect. 9

3 OPEN TRANSACTIONS ON INFORMATION PROCESSING 4. CLASSIFICATION OF DEFECTS The method which follows a two step process to detect and classify the defects has been proposed. In the first step, defects have been detected and in the second step, the defects have been classified. The separate handcrafted algorithm has been developed for classification of different defects. Step-I: Detection of defects in PCB image For the detection of defects, the test image (I T ) is compared with the base image (I B ) using image subtraction method. Two output images are obtained (positive image (I P ) and negative image (I N ) as shown in the following equations [15]: I P = I B I T (3) I N = I T I B (4) The addition of the positive image and the negative image gives all the detects present in the test image. The defects are stored in an intermediate image named detection image (I D ). The whole process of detection has been shown in Figure 1. I D = I P + I N (5) Step-II: Classification of Defects The various algorithms have been developed for classification of different defects presented as under: A. Classification of wrong hole size defects For the classification of wrong hole size the process has been graphically shown in Figure 2. In this IP and IN images has been used along with the complement of test image (ICT). Flood fill operation (fill all the empty spaces and holes) has been applied on the complement of the test image (ICTF). The IN image has been subtracted from the ICTF image presented difference image (IDCF). Finally the result of IDCF has been subtracted from ICTF. The wrong hole size defects has been presented by the output image(iwhd). I DCF = I CT F I N (6) I WHD = I CT F I DCF (7) B. Classification of etching defects For the classification of etching defects the process has been graphically shown in Figure 3. In this I P and I N image has been used along with the complement of base image (I CB ). Flood fill operation has been applied on the complement of the base image (I CBF ). The I CBF image has been subtracted from I P image to present the output image(i ED ) as etching defects. I ED = I P I CBF (8) 10 C. Classification of missing hole defects For the classification of missing hole defects the process has been graphically shown in Figure 4.

4 Detection and Classification of Printed Circuit Boards Defects Figure 1. Flowchart for detection of defects in test image[15] Figure 2. Flow chart for the classification of wrong hole size defects [15] In this I P and I N image has been used along with the complement of base image (I CB ). Flood fill operation has been applied on the complement of the base image (I CBF ). The I CBF image has been subtracted from I P image to present the output image(i ED ) as etching defects. Finally I ED has been subtracted from I P. The missing hole defects have been presented by the resultant image(i MHD ). Figure 3. Flowchart for the classification of etching defect[15] 11

5 OPEN TRANSACTIONS ON INFORMATION PROCESSING Figure 4. Flowchart for the classification of missing hole defects [15] Table 2. RESULT OF NORMALIZED CROSS CORRELATION Test Image Correlation Result Defective Defective Defective Defective Defective 6 1 Not Defective Defective I ED = I P I CBF (9) I MHD = I P I ED (10) D. Classification of missing conductors & break lines The remaining defects can be classified by the addition of all the defects classified (results of Figure 2, Figure 3 and Figure 4 ) and then subtracted from all the possible defects (result of Figure 1 ). 5. EXPERIMENTAL RESULTS The proposed method was tested using a number of faulty PCB images.the faulty PCBs were manually generated from fault free PCB images. First the normalized cross correlation has been calculated for different test images of PCBs as shown in Table II. And then image subtraction method has been implemented on test images of PCBs. The results of different test images (shown in Figure 5 - Figure 12 ) in terms of detection has been visually shown in the Figure 13 - Figure 19. From the Table 2, it has been shown that test images 1-5 and test image 7 have defects but test image 6 has no defect. So the image subtraction method can be implemented on defective PCBs only to detect the defects. 12

6 Detection and Classification of Printed Circuit Boards Defects Figure 5. Base Image Figure 6. Test Image 1 Figure 7. Test Image 2 Figure 5 shows base image which has been considered as the reference image for all the test images. The faulty test images with defects like missing hole, over and under etching, wrong hole size defects, missing conductor and break lines have been shown in Figure 6 - Figure 12. Figure 8. Test image 3 Figure 9. Test image 4 Figure 10. Test image 5 Figure 11. Test image 6 Figure 12. Test image 7 Step-I: Defect Detection Using Image Subtration The different defects detected from the test images by using image subtraction method have been shown in Figure 13 - Figure 19. Step-II: Classification of Defects After the detection of all defects using image subrtation method, the defects should be classified in different groups. For the classsification of defects, the test image 1 has been taken as considereation and follow up by the processed algorithm shown in Figure 1 - Figure 4. In this paper, the different defects like etching defects, missing hole defects, wrong hole size, missing conductors and break lines has been classified. The visual results of various steps in classification have been shown in Figure 20 - Figure 25. From these figures it has been proved that the proposed method is able to classify all the faults in the test image 1. 13

7 OPEN TRANSACTIONS ON INFORMATION PROCESSING Figure 13. Detection of defects in test image 1 Figure 14. Detection of defects in Figure 15. Detection of defects in test image 2 test image 3 Figure 16. Detection of defects in Figure 17. Detection of defects in test image 4 test image 5 Figure 19. Detection of defects in test image 7 Figure 18. Detection of defects in test image 6 Figure 20. Positive image Figure 21. Negative image Figure 22. Classification of Etching Defects 14

8 Detection and Classification of Printed Circuit Boards Defects Figure 23. Classification of Missed hole defects Figure 24. Classification of Figure 25. Classification of Missing Wrong hole defects Conductor & break lines 6. CONCLUSIONS & FUTURE WORK It is concluded from the results that the use of NCC make the defect detection and classification process more time efficient. The detection and classification results of proposed method are promising.most of the defects like over and under etching,missing conductors,breaklines, wrong hole are successfully detected without any misclassification. The proposed method has some drawbacks like it require the same size of base and test images. And it requires orientation of test image and base image. Future work can be done to overcome these drawbacks. ACKNOWLEDGEMENTS Authors thankfully acknowledge the suggestions given by the unknown learned reviewers. References [1] A. P. S. Chauhan and S. C. Bhardwaj, Detection of bare pcb defects by image subtraction method using machine vision, in Proceedings of the World Congress on Engineering, vol. 2, [2] J. W. Foster III, P. Griffin, and J. Korry, Automated visual inspection of bare printed circuit boards using parallel processor hardware, The International Journal of Advanced Manufacturing Technology, vol. 2, no. 2, pp , [3] D.-M. Tsai and C.-H. Yang, A quantile quantile plot based pattern matching for defect detection, Pattern Recognition Letters, vol. 26, no. 13, pp , [4] D.-M. Tsai and C.-T. Lin, Fast normalized cross correlation for defect detection, Pattern Recognition Letters, vol. 24, no. 15, pp , [5] A. Nakhmani and A. Tannenbaum, A new distance measure based on generalized image normalized cross-correlation for robust video tracking and image recognition, Pattern recognition letters, vol. 34, no. 3, pp , [6] D.-M. Tsai and R.-H. Yang, An eigenvalue-based similarity measure and its application in defect detection, Image and Vision Computing, vol. 23, no. 12, pp , [7] J. Jiang, J. Cheng, and D. Tao, Color biological features-based solder paste defects detection and classification on printed circuit boards, Components, Packaging and Manufacturing Technology, IEEE Transactions on, vol. 2, no. 9, pp , [8] C. Benedek, Detection of soldering defects in printed circuit boards with hierarchical marked point processes, Pattern Recognition Letters, vol. 32, no. 13, pp ,

9 OPEN TRANSACTIONS ON INFORMATION PROCESSING [9] W. Hao, Z. Xianmin, K. Yongcong, O. Gaofei, and X. Hongwei, Solder joint inspection based on neural network combined with genetic algorithm, Optik-International Journal for Light and Electron Optics, vol. 124, no. 20, pp , [10] G. K. B. Kaur and A. Kaur, Detection and classification of printed circuit board defects using image subtraction method, [11] F. Guo and S.-a. Guan, Research of the machine vision based pcb defect inspection system, in IEEE International Conference on Intelligence Science and Information Engineering, pp , IEEE, [12] Z. Ibrahim, S. Al-Attas, and Z. Aspar, Analysis of the wavelet-based image difference algorithm for pcb inspection, vol. 4, pp , [13] S. H. I. Putera, S. F. Dzafaruddin, and M. Mohamad, Matlab based defect detection and classification of printed circuit board, [14] Z. Ibrahim, S. Al-Attas, and Z. Aspar, Analysis of the wavelet-based image difference algorithm for pcb inspection, vol. 4, pp , [15] G. K. B. Kaur and A. Kaur, Study and analysis of defect detection in printed circuit boards using image subtraction method, International conference on Control, Communication and Computer Technology. 16