The objective of this project is to develop a predictive model for coating degradation and subsequent substrate corrosion on Army ground and rotary wing assets. The model would utilize historical data from past research of the Chemical Agent Resistant Coating (CARC) system applied to these types of equipment in conjunction with application variability, and operational exposures to model general degradation mechanisms. 

Technical Approach

This project will focus on ground and rotary wing assets for the Army to provide a comprehensive model for Army assets that utilize the CARC system. The Army model would complement the model being developed jointly by the Air Force and Navy, which is focused on fixed wing aircraft and ground support assets. A Bayesian approach is planned for the development of a predictive corrosion model for ground and rotary wing Army assets. This model would leverage the investigator's historical knowledge in the development and evaluation of CARC systems. The model data would also leverage the Army ground vehicle survey data, which was started in 2015, and at project start had inspected nearly 7,000 assets, identified over 95,000 parts having corrosion and coating damage, and contains over 140,000 photographs of corrosion and coating damage on parts. A major focus of this project is to collect data to characterize the initial condition of as-painted assets to identify areas and parts where corrosion is most likely to occur. Use of these data and additional testing (to fill identified gaps), will allow for the development of a model to predict coating degradation and asset corrosion.


The model will aid in optimizing repainting operations at Army facilities, which has the benefit of reducing worked exposure and emissions of hazardous materials. Development of a simple-to-use model, using a CBM+ approach, will encourage implementation by field and depot activities, maximizing life-cycle savings (minimize maintenance costs while maximizing corrosion performance). 

  • Corrosion ,

  • Inspection ,

  • Computational Methods ,

  • Failure Analysis