The environmental and mechanical loading conditions govern the overall lifetime survivability and maintenance cycles of protective coatings. A model that can better predict maintenance based on accumulated damage will enable maintenance cycles to be performed only when necessary as opposed to overly conservative periodic time-based maintenance intervals based on worst case scenarios. Using a Condition Based Maintenance Plus (CBM+) approach reduces the exposure of both personnel and the environment to hazardous paint strippers and hexavalent chromium used in many primers. By evaluating each asset based on service history and expected future exposure, maintenance will be done when required based on usage/exposure history. To achieve the desired state, there is a need to develop better modeling of coating and material lifetime performance. Furthermore, a combined model real-world fatigue and corrosion damage for Department of Defense (DoD) assets does not exist. By generating data which simulates a real-world environment and inputting this information into a predictive model, better decisions can be made as to the service intervals and reducing the amount of man-hours spent on systems with potentially hazardous materials to refurbish the asset. Additionally, the overall approach is generalizable and can be extended to other coating systems based on additional characterization data.

The objective of this project is to generate a Bayesian network model to predict coating performance and lifetime based on a CBM+ approach.

Technical Approach

Physics-based models will be used to supplement the Bayesian model and will provide more thorough examinations of the key contributors to atmospheric corrosion and coating degradation. Data input into the model will begin with known degraders of system life performance and then will be refined by datasets generated by combined mechanical and environmental exposure tests. The model will incorporate cumulative damage from aged specimens from both the Air Force and Naval Air community.


The resulting model could be used to better inform life-cycle maintenance costs and for adjusting the schedules for planned maintenance. Successful implementation will reduce costs of inspection, unnecessary repainting, and better prioritization of maintenance of key assets.