Autonomous Component Replacement Engine for Army Equipment Sustainment

Mobilio proposes develop an autonomous software tool for identifying and replacing obsolete electronic components in Army equipment. The tool can be used by LEAD as it reverse-engineers electronic and electro-mechanical systems in Army assets to predict which microelectronic components in use in Army systems are at risk of obsolescence, and to select new off-the-shelf components from other suppliers that can replace those components. Kipo AI extracts data from various supplier data sources, using advanced machine learning models to distill data from technical datasheets, drawings, and user-added annotations and specifications into a large, highly detailed library of electronic components integrated with engineers’ computer-aided design workflows. Kipo AI leverages this component library to reliably and rapidly identify new components that can serve as “drop-in” replacements for components that have become obsolete or are at risk of obsolescence, automating the laborious and expensive task of sourcing replacement components by searching through supplier data manually. Using machine learning models trained on historical and market data as well as technical specifications, the system also assesses obsolescence risks and forecasts which components may become unavailable, allowing LEAD to strategically plan for potential future obsolescence, stockpile components to mitigate against supply chain disruptions, and eliminate vulnerable parts in field-deployed assets. If implemented, this solution will help the Army foresee and mitigate future part obsolescence and reverse-engineer circuit cards and wire harnesses more rapidly, supporting the Army in strategically planning sustainment operations and in returning equipment to the field more quickly. Kipo AI’s autonomy-driven approach to obsolescence management for electronics can help the Army reduce equipment downtime and drive efficiency, enhancing military readiness.