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SOURCE: RAUNAK KUNDE / NEWS BEAT / IDRW.ORG

The Indian Air Force (IAF) is exploring cutting-edge technology to revolutionize its maintenance and inventory management systems. Aimed at enhancing operational efficiency and reducing costs, this initiative involves creating digital twins of aircraft parts, implementing advanced scanning technologies, and leveraging artificial intelligence (AI) for inventory optimization. Air Marshal Mohan recently outlined the IAF’s vision, highlighting the move from traditional hardware tree maintenance systems to predictive, data-driven solutions that promise to streamline and optimize aircraft maintenance.

In the current maintenance management system, each aircraft component is mapped in a “hardware tree” structure that catalogues the thousands of individual parts, like engine components, required for operational readiness. However, the IAF envisions an upgrade—where aircraft systems will be able to interact directly with a digital hardware tree and a digital twin, thereby transforming maintenance from reactive to predictive.

A digital twin is a virtual replica of a physical asset that continuously receives real-time data from the equipment it mirrors. This digital model allows for advanced simulation, analysis, and predictive maintenance. With a digital twin in place, the IAF aims to receive precise, actionable maintenance recommendations, enabling preventive measures before issues arise. This capability is anticipated to extend the lifespan of critical components, increase aircraft uptime, and improve overall mission readiness.

Another component of the IAF’s digital maintenance initiative is the “digital fingerprinting” of aircraft parts. Currently, multiple aircraft types may use identical parts with different part numbers, complicating parts management. Digitally fingerprinting these components would involve assigning each part a unique digital identity, making it possible to cross-reference across various systems, streamline inventory, and avoid redundancy.

The digital fingerprinting process, which is being developed through a pilot project with IIT-Bombay, involves creating 3D digital models and capturing the material composition of each part. Advanced scanners produce these 3D drawings, while X-ray scanners reveal the material composition, providing a comprehensive digital blueprint. This blueprint is then linked to a Global Item Gallery (GIG) number for simplified identification across the IAF’s entire inventory. By reducing duplicate parts and standardizing parts management, the IAF can optimize its inventory and minimize logistical complexities.

With the use of AI algorithms, the IAF intends to conduct an exhaustive scan of its digital parts library. By analyzing digital drawings and GIG numbers, AI can quickly identify identical or interchangeable parts, even if they have been assigned different part numbers. This capability can substantially reduce the volume of IAF’s inventory, freeing up storage space, reducing costs, and ensuring parts availability for urgent maintenance needs.

One of the critical challenges with inventory optimization is ensuring that similar-looking parts perform to required standards, particularly for high-stress engine components. Many parts may appear identical but could exhibit different properties under specific conditions like heat treatment. This aspect is crucial in aircraft engines, where material properties under extreme conditions can directly impact safety and performance.

To address this, the IAF is collaborating with IIT-Bombay to conduct a detailed microstructure analysis of these components. By using specialized lab equipment to analyze and certify the microstructure of parts, IIT-Bombay can verify their performance characteristics. This testing process ensures that only parts meeting exact standards are deployed, reinforcing the reliability and safety of IAF aircraft.

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