SBIR-STTR Award

There is a tremendous need to identify and optimize the factors that affect human performance in maintenance and inspection. Innovative solutions that highlight and track corrosion issues are required in order to enhance the maintainers ability in the prevention, inspection, removal and treatment of corrosion and information management. This proposal describes an approach utilizing Artificial Intelligence (AI) and Statistical methods that the authors have successfully utilized in predicting the deterioration of Navy ship tank and void services and Army/ Marine Corp wheeled vehicles. In addition, the methods we created for aircraft inspection and data mining of the resulting depot level scoring, induction inspections and maintenance for the Navy and Air Force will also be used in the development of an Aircraft Corrosion Prediction and Simulation software tool. This tool will predict and display the corrosion hotspots on the aircraft as they evolve. The proposed technology should result in substantial decreases in maintenance costs associated with detecting, repairing, and tracking corrosive areas.

Benefit:
The first round of customers for this product is the Navy, Coast Guard, Army and Air Force aircraft programs both existing and new procurements. The product will be use within these procurement programs to evaluate corrosion control materials, treatments, and processes. It can also be used in the research and development of new environmentally friendly, corrosive-resistant materials and in evaluating alternative corrosion control technologies. On a boarder scope the final product will be applicable to other assets within the Navy and DoD beyond aircraft including facility operations. The software will contain the tools to predict the deterioration of any asset including non-metals such as cement, underground piping, bridges, offshore oil rigs, plastics, etc.

Keywords:
corrosion, corrosion,, statistical analysis, Naval Aviation, Artifical intelligence, maintenance

This proposal describes the development of aircraft simulation and modeling analysis system for predicting the degradation of aircraft structures due to corrosion. This will be achieved by extending the existing ACES software product developed by GCAS under Army contract to NAVAIR aircraft structures, materials and corrosion failure modes. This includes identifying and modeling the detailed failure mechanisms for different forms of corrosion and material degradation relevant to aircraft including Pitting, Stress Corrosion Cracking and Exfoliation which are the primary sources of corrosion related failures on aircraft. The existing ACES product already includes methods for simulating uniform, galvanic and crevice corrosion. The software will include parallel computing versions of the proposed geometry search and feature recognition algorithms for use on HPC or GPU architectures and a Knowledge Acquisition Facility that will allow end user organizations to update the Knowledge Base with new information as it becomes available in the future.

Benefit:
The proposed Accelerated Corrosion Expert Simulator for aircraft systems will have significant benefit and payback to NAVAIR by providing a tool to simulate the advent of corrosion on aircraft structures over time. The benefit will come in the form of time and cost savings in anticipating corrosion during fleet and depot level inspections, improved understanding of the nature of aircraft corrosion due to pitting, SCC and exfoliation, a knowledge acquisition and retention system that will continue to grow over time. The benefit of the developed system extends far beyond NAVAIR to all agencies of DoD maintaining aircraft fleets (Army Aviation and Missile Command (AMCOM) and Air Force, Coast Guard and Marine Corps), including the aircraft manufacturers and their subs; and commercial aircraft companies.

Keywords:
IRCMS, MRP-II, Stress Corrosion Cracking, Simulation and Modeling, aircraft corrosion, Forecasting, learning algorithms, ADCS, Prediction Algorithms, Exfoliation, pitting, Knowledge Acquisition, DECKPLATE