Reconstructing the 3D solder paste surface model using image processing and artificial neural network
Journal
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Journal Volume
3
Pages
3051-3056
Date Issued
2004
Author(s)
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. ? 2004 IEEE.
Subjects
Automatic optical inspection (AOI)
Learning architecture models
Solder paste inspection
Three dimensional (3D) reconstruction
Computer architecture
Computer vision
Feature extraction
Image processing
Image reconstruction
Laser beams
Learning systems
Mathematical models
Three dimensional
Virtual reality
Neural networks
Type
conference paper