PCB Inspection using Competitive Learning and Fuzzy Associative Memories
Abstract
Visual printed circuit board inspection in digital images is viewed as a pattern classification problem. The process involves a two level classification of the printed circuit board image sub-patterns into either standard non-defective class or a defective class. The patterns that are identified as being defective in the first level are thoroughly checked for defects in the second level. And the patterns that are non-defective are checked for dimensional verification for the classes it has been identified and assigned to the correct class. This classification uses fuzzy information of the sub-patterns by extracting features from the scan-line grid. A prototype system is development using fuzzy associative memories for dimensional verification. Rules are generated using the features extracted from the scan-line grid with an adaptive vector quantization algorithm that uses differential competitive learning (DCL) in updating winning synaptic vectors. The objective in using fuzzy feature vectors is to drastically reduce the computational time required for inspection, which is a major problem in the conventional inspection systems. The paper concludes with experimental results and directions for future work.
Recommended Citation
M. Moganti et al., "PCB Inspection using Competitive Learning and Fuzzy Associative Memories," Artificial Neural Networks in Engineering - Proceedings (ANNIE'94), vol. 4, pp. 421 - 426, Dec 1994.
Department(s)
Engineering Management and Systems Engineering
Second Department
Computer Science
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Publication Date
01 Dec 1994