Neural-network-based parts classification for SMT processes

Byungman Kim; Hyungsuck Cho

doi:10.1117/12.444097

4 October 2001 Neural-network-based parts classification for SMT processes

Byungman Kim, Hyungsuck Cho

Proceedings Volume 4564, Optomechatronic Systems II; (2001) https://doi.org/10.1117/12.444097
Event: Intelligent Systems and Advanced Manufacturing, 2001, Boston, MA, United States

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.

Citation Download Citation

Byungman Kim and Hyungsuck Cho "Neural-network-based parts classification for SMT processes", Proc. SPIE 4564, Optomechatronic Systems II, (4 October 2001); https://doi.org/10.1117/12.444097

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