Development of moire fringe recognition system using artificial neural network for 2 d displacement measurement
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Date
2018-04-01
Authors
Woo Wing Hon
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Abstract
Various methods have been proposed in the analysis of moiré pattern. These
methods can be categorized into manual inspection by human inspector, computational
methods and image analysis based methods. Manual interpretation of moiré patterns
is prone to human errors as it is highly dependent on the decision of the human
inspector. The computational methods are lack of flexibility as they are limited to high
frequency gratings which are sinusoidal in the transmittance of grating. As for the
image analysis based methods, complex algorithms can unintentionally remove the
fine details in the moiré patterns and cause uncertainty in the analysis. To overcome
the above mentioned drawbacks, an artificial neural network (ANN) approach is
proposed for a moiré fringe recognition system in 2-D displacement measurement. The
moiré fringe recognition system consists of two ANNs with two different tasks : (i)
the determination of moiré fringe centers of the circular grating moiré patterns and (ii)
the determination of eccentricity magnitudes and eccentricity directions of the circular
grating moiré patterns. The ANN approach is compared to graphical analysis method
(GAM), an image analysis based method, in terms of accuracy and computational time
for 2-D displacement measurement of circular grating moiré patterns. The experiments
prove that ANN approach has a higher accuracy to GAM with mean errors with 95%
confidence of 0.068 ± 0.013 mm for eccentric magnitudes and 1.85 ± 0.465º. An
improvement of 66.18% in the computation time is also reported in the comparison. A
straightforward solution for the moire fringe recognition system of circular grating
moire pattern is achieved using ANN approach.