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| PREDICTIVE MODEL BASED ON WAVELET TRANSFORMATION AND ARTIFICIAL NEURAL NETWORK FOR ZINC ATMOSPHERIC CORROSION |
| Shouyan Wang;Shizhe Song |
| 天津大学材料学院 |
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Abstract A computation approach using wavelet transformation and artificial neural network (ANN) to analyze and quantify the atmospheric corrosion of zinc by corrosion morphology image is described.A scanner is used to obtain the corrosion morphology images of zinc samples.Multi-resolution wavelet analysis is performed on the selected image to obtain energy values as feature vectors.The canonical correlation analysis is used to gain the correlative coefficient between the features and the corrosion loss.The weighted features were analyzed to obtain the material loss due to corrosion by using an ANN model.The resu
lts indicated that the computational methods developed for corrosion analysis could provide reasonable results for estimating material loss due to corrosion morphological image.
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Received: 22 April 2004
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