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Modeling the Evolution of Corrosion: A Feature-Based Model for Growth

机译：腐蚀演变模型：基于特征的增长模型

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The Government Accounting Office reports that the Department of Defense spends approximately $20 Billion per year on prevention and repair of corrosion. The resources required to combat corrosion problems are desperately needed for equipment and personnel in today's high operations tempo environment. Despite its significance, modeling for the prediction of corrosion evolution has been largely ignored in the literature. In this work, a feature- based model for event initiation and growth in space and time is developed. The initiation model is based on previous research for feature-based prediction of event locations. The growth model expands the basic interacting particle system definition to incorporate feature information using a growth probability function defined over the geographic region of interest. The inclusion of significant features improves the approximation of the function in terms of error and deviance reduction. The model is applied using data from images of filiform growth in samples of AA2024-T3. The first approach involves the derivation of features from macro scale images where the only distinguishable features are geometric in nature and derived from the filiform growth objects. Segmentation of individual filaments is not possible at this scale and a wide range of classification methods are considered to predict filiform growth using the derived feature information. The results show that the derived features at this scale are insufficient to capture the directed nature of the filiform growth process.

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