Reconstructing the 3D solder paste surface model using image processing and artificial neural network

Fang Chung Yang*, Chung Hsien Kuo, Jein Jong Wing, Ching Kun Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

In general, the laser Inspection can measure accurate 3D solder paste surface model, however, it is not practical due to the high cost and low inspection speed. This paper presents the three-dimensional (3D) solder paste surface model reconstruction using the image processing and artificial neural network (ANN), and the proposed approach forms the virtual laser 3D automatic optical inspection (AOI) model. The input nodes of the ANN model consist of the image features that are captured from images of using different light sources. The output nodes are the heights of the corresponding image pixel areas. The training patterns of the proposed ANN model use the laser 3D inspection results. Meanwhile, the in-lab design and the commercial coaxial light sources with the pad and sub-area based learning architecture models are constructed and validated, and the estimated 3D surface model achieves 90% accuracy in average.

Original languageEnglish
Title of host publication2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
Pages3051-3056
Number of pages6
DOIs
StatePublished - 2004
Event2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004 - The Hague, Netherlands
Duration: 10 10 200413 10 2004

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume3
ISSN (Print)1062-922X

Conference

Conference2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
Country/TerritoryNetherlands
CityThe Hague
Period10/10/0413/10/04

Keywords

  • 3D reconstruction
  • Artificial neural network
  • Machine vision
  • Solder paste inspection

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