JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Unified Approach to Path Planning Algorithm for SMT Inspection Machines Considering Inspection Delay Time
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Unified Approach to Path Planning Algorithm for SMT Inspection Machines Considering Inspection Delay Time
Lee, Chul-Hee; Park, Tae-Hyoung;
 
 Abstract
This paper proposes a path planning algorithm to reduce the inspection time of AOI (Automatic Optical Inspection) machines for SMT (Surface Mount Technology) in-line system. Since the field-of-view of the camera attached at the machine is much less than the entire inspection region of board, the inspection region should be clustered to many groups. The image acquisition time depends on the number of groups, and camera moving time depends on the sequence of visiting the groups. The acquired image is processed while the camera moves to the next position, but it may be delayed if the group includes many components to be inspected. The inspection delay has influence on the overall job time of the machine. In this paper, we newly considers the inspection delay time for path planning of the inspection machine. The unified approach using genetic algorithm is applied to generates the groups and visiting sequence simultaneously. The chromosome, crossover operator, and mutation operator is proposed to develop the genetic algorithm. The experimental results are presented to verify the usefulness of the proposed method.
 Keywords
AOI (Automatic Optical Inspection);path planning;clustering;traveling salesman problem;genetic algorithm;
 Language
Korean
 Cited by
 References
1.
N. S. S. Mar and P. K. D. V. Yarlagadda, "Design and development of automatic visual inspection system for PCB manufacturing," Robotics and Computer-Integrated Manufacturing, vol. 27, no. 5, pp. 949-962, 2011. crossref(new window)

2.
H. J. Cho and T. H. Park, "Wavelet transform based image template matching for automatic component inspection," The International Journal of Advanced Manufacturing Technology, vol. 50, no. 17, pp. 1033-1039, 2010. crossref(new window)

3.
J. S. Lee and T. H. Park, "Identification of component packaging region for electronic Assembly system," Institute of Control, Robotics and Systems, vol. 2013, no. 5, pp. 562-563, 2013.

4.
A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: a review," ACM Computing Surveys, vol. 31, no. 3, pp. 264-323, 1999. crossref(new window)

5.
T. Niknam, E. T. Fard, N. Pourjafarian, and A. Rousta, "An Efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering," Engineering Applications of Artificial Intelligence, vol. 24, no 2, pp. 306-317, 2011. crossref(new window)

6.
U. Maulik, "Genetic algorithm-based clustering technique," Pattern Recognition, vol. 33, no. 5, pp. 1455-1465, 2000. crossref(new window)

7.
J. Gu, "Efficient local search with search space smoothing: a case study of the traveling salesman problem (TSP)," Man and Cybernetics, vol. 24, no. 5, pp. 728-735, 1994. crossref(new window)

8.
M. Bellmore and G. Nemhauser, "The traveling-salesman problem: a survey," Operation Research, vol. 16, no. 6 pp. 538-558, 1968. crossref(new window)

9.
A. Otman and A. Jaafar, "A comparative study of adaptive crossover operators for genetic algorithms to resolve the traveling salesman problem," International Journal of Computer Applications, vol. 31, no. 11, pp. 49-57, 2011.

10.
H. J. Lin, F. W. Yang, and Y. T. Kao, "An efficient GA-based clustering technique," Tamkang Journal of Science and Engineering, vol. 8, no. 2, pp. 113-122, 2005.

11.
M. Laszlo and S. Mukherjee, "A genetic algorithm that exchanges neighboring centers for k-means clustering," Pattern Recognition, vol. 28, no. 16, pp. 2359-2366, 2007. crossref(new window)

12.
L. E. Agustin-Blas and S. Salcedo-Sanz, "A new grouping genetic algorithm for clustering problems," Expert Systems with Applications, vol. 39, no. 120, pp. 9695-9703, 2012. crossref(new window)

13.
H. K. Tsai and J. M. Yang, "Some issues of designing genetic algorithms for traveling salesman problems," Soft Computing, vol. 8, no. 8, pp. 689-697, 2003.

14.
S. Yuan and B. Skinner, "A new crossover approach for solving the multiple travelling salesmen problem using genetic algorithms," European Journal of Operational Research, vol. 228, no. 7, pp. 72-82, 2013. crossref(new window)

15.
T. H. Park, H. J. Kim, and N. Kim, "Path planning of automated optical inspection machines for PCB assembly systems," International Journal of Control, Automation, and systems, vol. 4, no. 1, pp. 96-104, 2006.