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Bayesian Modeling Coating Performance

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Coating degradation on Army ground systems represents a significant maintenance cost and effort. The objective of this proposed work is to develop a predictive model for coating degradation and subsequent substrate corrosion on Army ground assets. Provided with a better understanding of the root causes, steps can be taken to reduce corrosion impacts on Army materiel.

Product Number: 51322-17612-SG
Author: James A. Ellor, Daniel Pope, Anthony Florimbio, C.Thomas Savell, Lisa A. Barker, John Repp
Publication Date: 2022
Industries: Corrosion Monitoring and Control , Coatings
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The research describes the effort to develop a predictive model for coating degradation and substrate corrosion on Army assets. The model incorporates the learning from field surveys of over 15,000 assets and 250,000 components; coating performance in standardized testing; and observations of coating condition as-applied to fielded items. The model outputs would provide a basis to (1) support a Commodity Manager to determine repaint intervals, optimizing expenditures and (2) develop new products / processes (impacting coating performance) increasing life of an asset protective coating system.

The research describes the effort to develop a predictive model for coating degradation and substrate corrosion on Army assets. The model incorporates the learning from field surveys of over 15,000 assets and 250,000 components; coating performance in standardized testing; and observations of coating condition as-applied to fielded items. The model outputs would provide a basis to (1) support a Commodity Manager to determine repaint intervals, optimizing expenditures and (2) develop new products / processes (impacting coating performance) increasing life of an asset protective coating system.

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