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Proceedings Paper

Neural-network-based parts classification for SMT processes
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Paper 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.

Paper Details

Date Published: 4 October 2001
PDF: 12 pages
Proc. SPIE 4564, Optomechatronic Systems II, (4 October 2001); doi: 10.1117/12.444097
Show Author Affiliations
Byungman Kim, Korea Advanced Institute of Science and Technology (South Korea)
Hyungsuck Cho, Korea Advanced Institute of Science and Technology (South Korea)


Published in SPIE Proceedings Vol. 4564:
Optomechatronic Systems II
Hyungsuck Cho, Editor(s)

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