Battle-Damage Data Recording System (BDDRS)

NAVAIR has a stated need for expedient battle damage analysis and uses a variety of techniques to assess issues, including non-destructive inspection systems and methods as well as visual inspection. Industry continuously develops new and better inspection technologies, such as paint integrity assessment systems for traditional and low observable coatings, which the Navy may implement as well. ARCS understands there is interest in a short-term maintenance solution to keep planes in a state of rapid readiness in a wartime scenario. We concluded that NAVIR needs a system to identify, record, and communicate battle-related damage data within a system that is easy to use, intuitive and non-complicated, and practical for a contested environment where time is of the essence to the technicians and the environment in which assessments are being done is less than ideal. This will be a single dynamic, flexible, yet durable system that is easy to use by both technical professionals and non-technical people who are ōpitching inö under duress in a contested environment is needed to record damage data and subsequently leverage that damage data to drive repair of battle-damaged aircraft. ARCSÆ approach to this challenge will be to create a system that can run on multiple platformsŚprimarily on tablets and phones, but also AR goggles if conditions permitŚaccepting assessment data from multiple sources such as text, voice recording, or video, and transmit to other using only local connectivity, if even needed. Menus, terms, labels, and icons will be easily understood and intuitive. In effect, we will build a simple front end to a robust system that delivers critical utility to the contested area maintainer. ARCS will modify our existing maintenance data recording/tagging systems to facilitate the recording of data from multiple known and to be determined systems, capable of use on a variety of platforms (tablets, phones, and more), designed for use by technical and non-technical personnel, and over time evolved with Artificial Intelligence and Machine Learning (AI/ML) to suggest likely areas of damage progressionŚall with the goal of optimizing the efficiency of what can be repaired and how quickly, to improve fleet readiness. ARCSÆ application will maintain a spatially-oriented dataset as damage inspectors enter information related to damages into their tablets and phones. The inspectors will have access to a simplified 3D model of the aircraft, with each damage location marked as a visual indicator on the model. By tapping on a marker, all relevant data and images associated with that location can be retrieved. The marker's appearance will visually represent the type of damage at that location.??????? The system's Logic Processes, Machine Learning, and Artificial Intelligence will significantly augment leaders' decision-making capabilities. These technologies will analyze past data and future usage trends to offer valuable assistance and proactive suggestions.