Applying Generative AI to Digital Twin Development for Design and Sustainability

Given the recent advancements in open-sourced natural language processing, image processing, and generative AI, Qualtech Systems, Inc. (QSI) proposes to explore options in generating equipment digital twins, or pieces and properties of them, directly from unstructured data, such as technical documentation in the form of PDFs, schematics, maintenance records or any other unstructured data source. We believe that our effort will significantly enhance the scaling of development of high-fidelity digital twins and hence their adoption for the equipmentĺs (physical twin) entire life cycle. This proposal will cover our plans for doing so, and how that aligns with the Department of Defenseĺs goals and priorities vis-a-vis the adoption of digital twins as one of the key enablers of US military supremacy.QSI will leverage its TEAMS« Digital Twin-driven Equipment Diagnostics and Prognostics Analyses and Intelligent Guided Troubleshooting Tool Set in use at NASA, DOD and commercial entities. For this effort, QSI will also leverage its subject matter expertise, and substantial equipment literature that includes, among others, technical manuals and schematics on a variety of Army and Navy vehicles and systems.áTEAMS« digital twins facilitate sustainable and maintainable equipment design and all operational maintenance decision-making to help the Department of Defense in your goal of ôdelivering on time, every time, to preserve our national securityö. The TEAMS« suite leverages digital twins to reason about failures in systems, performing analyses and provide rapid insight for equipment design and maintenance that have been developed out of 30 years of solving business problems for various Fortune 500 companies, DoD entities, and other government agencies such as the DoE and NASA. The insights produced by our software are actionable and have benefited the maintenance and logistics departments of our customers in numerous ways.