Optimize Position and Path Planning of Automated Optical Inspection

Transcription

1 Journal of Computational Information Systems 8: 7 (2012) Available at Optimize Position and Path Planning of Automated Optical Inspection Hao WU, Yongcong KUANG, Gaofei OUYANG, Hongwei XIE, Xianmin ZHANG Key Laboratory of Precision Equipment and Manufacturing Technology of Guangdong Province, South China University of Technology, Guangzhou , China Abstract A three layers of MARK point method is proposed to improve the printed circuit board(pcb) position accuracy of automated optical inspection(aoi).to minimize the overall working time, the path planning problem can be modeled as a standard travelling salesman problem (TSP), and a new method based on Hopfield net algorithm is proposed to optimize the path planning problem. The experiments show that the three layers of MARK point improve the position accuracy and the Hopfield net optimize the planning path. Keywords: Automated Optical Inspection; Three Layers of MARK Point; Hopfield Net 1 Introduction In recent years, PCB(printed circuit board) assembly using SMT(surface mount technology) is very popular in electronic manufacturing [1],the size of the component assembled on the PCB become more and more small[2, 3]. The automated optical inspection (AOI) [4] has been applied to replace the traditional visual inspection. The position accuracy of the PCB has great influence on the inspection process, and the path planning problem will influence the overall working time of AOI. The traditional position method is using the MARK point to adjust the PCB in the loading process [5], but the position error produced by single board assembly and partial uneven surface in the board cannot be compensated with the whole board alignment only. In order to decrease the position error in these situation, the three layers of PCB MARK point method was proposed. To minimize the overall working time, the path planning is to find the optimal path. The path planning includes the position of the inspection windows and the planning of visiting sequence path. In this paper, the inspection windows are determined previously, so we focus on solution to the planning of visiting sequence path. Corresponding author. address: zhangxm@scut.edu.cn (Xianmin ZHANG) / Copyright 2012 Binary Information Press April 2012

2 2958 H. Wu et al. /Journal of Computational Information Systems 8: 7 (2012) Tae-Hyoung Park and Hwa-Jung Kim [6] proposed a unified method to determine the inspection clusters and visiting sequence simultaneously. A hybrid genetic algorithm was applied to solve the highly complicated optimization problem. The optimal solution quality was improved significantly. However, the solving time was too long. Luo Bing and Zhang Yun [7] proposed to do the path planning in two steps: at first to determine the least number of inspection windows based on ant colony algorithm; then combined with gradually approaching method to seek the shortest inspection path and to fix every inspection window. The solution was very well but the solution process was too complicated and timeconsuming. Tapas Kanungo, David M. Mount and Nathan S. Netanyahu et al [8] presented a simple k- means clustering method to minimize the inspection windows, the solution was efficient but easily entrapped into the local optimal. To improve the efficiency of the solution, we proposed a new method to solve the visiting sequencing problem. The Hopfield net algorithm [9] is applied to solve the optimization problem. The experimental results verify the proposed method efficiency and useful using of a commercial machine. 2 Position Compensation There are three different layers of MARK point in the checking process. The first layer is the whole board MARK point, which compensates the error in loading the PCB board. The second layer is the single board MARK point, which compensates the error in the single board assembly. The last layer is partial FOV MARK point, which compensates the error caused by uneven surface in some area of the PCB board. After compensating the three levels MARK point compensation, the accurate position of the checking components are fixed. Then the checking result is obtained after inspection of the component. The process is shown in Fig.1. Fig. 1: Workflow of the PCB checking process The position error of each inspection component also was obtained in this inspection process, the position error P was calculated in Eq. 1

3 H. Wu et al. /Journal of Computational Information Systems 8: 7 (2012) P = P P (1) P is confirmed in the programmed configuration, P can be ensured in the inspection process. The position error P obey the normal distribution. So the positioning performance can be estimated by the standard deviation of statistic all the components position error. 3 Path Planning Problem Fig.2 shows a typical AOI machine that consists of a gantry and a camera. The gantry moves in x-direction, and the camera moves along the gantry in y-direction. These x and y-direction movements can occur currently. Since the FOV (field of view) of camera is bounded by its limit, the camera travels over whole area of board to acquire overall images. Fig. 2: Structure of AOI machine Fig.3 shows inspection windows, FOV and camera path the AOI machine. The inspection window is rectangle area to be inspected by camera, which includes component and soldering pad. Several hundreds or thousands of windows are usually located in one PCB. FOV is the maximum image area that can be acquired by one shot of camera. The size of FOV is a constant parameter of camera. The inspection cluster is a group of inspection windows that can be captured by one shot of camera. So the size of FOV limits that of the inspection cluster. The camera starts from a given waiting position, and visits every cluster to acquire image data for all inspection windows. The camera path is the sequence of clusters visited by camera. Fig. 3: Inspection windows, FOV and camera path

4 2960 H. Wu et al. /Journal of Computational Information Systems 8: 7 (2012) The sequencing problem can be modeled as a standard travelling salesman problem (TSP). Therefore we can apply directly the well-known Hopfield neural network model to the sequencing problem, then an optimal path can be got using this model. The continuous Hopfield neural network has the optimization calculation characteristics. The target function of TSP responds to the Hopfield net energy function [10, 11]. The sequence of the cities responds to the Hopfield net neurons state. According to the stability of the Hopfield net, when the energy function reaches the minimum value, the whole neurons states approach to balance, the order of the cities is the optimal path. The whole process is shown in Fig.4 (1) Model mapping Fig. 4: Process of hopfield net optimization In order to map the TSP to the dynamic process of the Hopfield net, the relation matrix was introduced. Using N N matrix represent the N cities. Table 1 show that there are 5 cities A, B, C, D, E,the visiting route is A C E D B. Table 1: Visiting route of 5 cities order city A (2) Build energy function and dynamic equation B C D E The energy function is corresponding to the target function (the optimal path). Meanwhile, there is only a 1 in each column and each row. The energy function E includes the restraint and the target: E = A 2 N N ( V xi 1) 2 + A 2 x=1 i=1 N N ( V xi 1) 2 + D 2 i=1 x=1 N N N V xi d xy V y,i+1 (2) x=1 y=1 i=1 A = 200, D = 100 The dynamic equation is: du xi dt = E V xi = A( N N V xi 1) A( V yi 1) D i=1 y=1 N d xy V y,i+1 (3) y=1 (3) Initialize the Hopfield net

5 H. Wu et al. /Journal of Computational Information Systems 8: 7 (2012) Based on the experiences, the initial input of the net is: U xi (t) = U 0 ln(n 1) + δ xi (x, i = 1, 2,..., N; t = 0) (4) U 0 = 0.1; N is the number of cities; δ xi is a random value between -1 and 1 (4) Optimal calculation When the net structure and parameter is determined, the optimal calculation is: Step 1: according to the position of the N cities, compute the distance between cities; Step 2: initialize the Hopfield net; Step 3: calculate the dynamic equation according to the Eq.3, and using the first order Euler equation to iteration: Step 4: calculate V xi (t) by U xi (t + 1) = U xi (t) + du xi T (5) dt V xi (t) = g(u xi (t)) = 1 2 [1 + tan sig(u xi(t) U 0 )] (6) Step 5: acquired the energy function E according to Eq.2 Step 6: stopping criterion, if the iterations number k > 10000, then stop, otherwise, k = k +1, go back to step 3 4 The Experiments To evaluate the performance of the proposed method, The size of FOV is16(mm) 12(mm), and the image acquisition time for one FOV is 0.25 (sec). PCBs with size of 146(mm) 270(mm) in the SMT production assembly line were inspected by the AOI system[12,13], the size is normalization in path planning. Fig.5 shows the AOI system. As the alignment method mentioned above, the PCB location based on three levels of MARK point, the whole PCB board is consist of two single boards, which was shown in Fig.6. Fig. 5: The AOI system used in the experiment

6 2962 H. Wu et al. /Journal of Computational Information Systems 8: 7 (2012) Fig. 6: Three layers of MARK point The alignment effect of the whole board alignment only and the whole board combined the single board with the partial FOV alignment is shown below, the whole board alignment has the standard deviation of in axis x and in axis y ; in the condition of the whole board combine the single board with the partial FOV alignmentthe standard deviation is in axis x and in axis y separately. So the new three layers of MARK point have a higher position accuracy. The optimal path using the c-means and Hopfield net are shown in Fig.7 and Fig.8 separately. The optimal path using the c-means is ,the total length is The optimal path using the Hopfield net is ,the total length is The Hopfield net method acquired a more optimal result than the c-means method. Fig. 7: Optimal path using c-means method 5 Fig. 8: Optimal path using hopfield net method Conclusion In this paper, a new three levels of MARK point is proposed to improve the position accuracy caused by the partial uneven. Additionally, a new method based on Hopfield net is proposed to the path planning problem. The experimental results show that the position accuracy has

7 H. Wu et al. /Journal of Computational Information Systems 8: 7 (2012) been improved by using the three levels of MARK point, while the Hopfield net also acquires an optimizing path. Acknowledgement This work is supported by the National Science Foundation for distinguished young scholars of China ( ); the United Fund of NSFC and Guangdong province (U ), Project GDUPS (2010) and the Fundamental Research Funds for the Central Universities (2012ZP0004). The supports are greatly acknowledged. References [1] M. Magenta, F. Rectal and C. H. Dangle et al. Automatic PCB Inspection Algorithms: A Survey. Computer Vision and Image Understanding, 63(2), pages , [2] T. ZedniceK, P. Vasina and Z. Sita et al. Lead-free soldering effect to tantalum capacitors, 26th International Spring Seminar on Electronics Technology, pages 85-89, [3] J. Nguyen, D. Geiger, D. Rooney and D. Shangguan. Solder Joint Characteristics and Reliability of Lead-Free Area Array Packages Assembled Under Various Tin-Lead Soldering Process Conditions, 2007 Electronic Components and Technology Conference, pages , [4] D. W. Raymond, D. F. Haigh. Why automate optical inspection. International Test Conference, pages 1033, [5] Chang-bing Bai, Chun Qi. Fast detection of circular pcb mark using Hough transform. Optoelectronic Engineering, 32(9), pages 75-78, [6] Tae-Hyoung Park, Hwa-Jung Kim. Path planning of automatic optical inspection machines for PCB assembly systems IEEE International Symposium on Computational Intelligence in Robotics and Automation, pages , [7] Luo Bing and Zhang Yun. SMT automatic optical inspection path planning based on ant colony algorithm. Chinese Journal of Scientific Instrument, 27 (6), pages 94-97, [8] Kanungo. T, Mount. D. M and Netanyahu. N. S et al. An efficient k-means clustering algorithm: analysis and implementation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), pages , [9] J. J. Hopfield and D. W. Tank. Neural Computation of Decisions in Optimization Problems. Biological Cybernetics, 52(3), pages , [10] Wang Ling and Zheng Dazhong. Study on TSP and Optimization Based on Hopfield Neural Network. Control and decision, 14(6), pages , [11] Sun Shouyu, Zheng Junli. A modified algorithm and theoretical analysis for Hopfield network solving TSP. Acta Electronica Sinica, 23 (1), pages 73-78, [12] Fupei Wu and Xianmin Zhang. Feature-Extraction-Based Inspection Algorithm for IC Solder Joints. IEEE Transactions on components, packaging and manufacturing technology, 1(5), pages , [13] Hongwei Xie, Yongcong Kuang, Gaofei Ouyang et al. An incremental Clustering Based Solder Joint Inspection Algorithm for Chip component. Journal of Computational Information Systems, 7 (14), pages , 2011.