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Neural Network-Based Parts Classification for SMT Processes
| Content Provider | Semantic Scholar |
|---|---|
| Author | Cho, Hyung Suck |
| Copyright Year | 2003 |
| Abstract | With the increasing necessities for reliable PCB product, there has been a considerable demand for high speed, high precision vision system to place the electric parts on PCB automatically. To identify the electric chips with high accuracy and reliability with obtained images, a classification algorithm is needed to identify the type of parts and their defects. In this paper, we design a learning vector quantization (LVQ) neural network to achieve this. From the images obtained under the versatile lighting system, characteristic features for classification are extracted, from which type of chip is identified through the neural network based classification algorithm. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://koasas.kaist.ac.kr/bitstream/10203/1619/1/PSI000268.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Artificial neural network Biological Neural Networks Classification Extraction Image processing Learning vector quantization Neural Network Simulation Polychlorinated Biphenyls Printed circuit board Software bug Statistical classification corporation |
| Content Type | Text |
| Resource Type | Article |
