Bharat in Betastartup field guide
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No. 722Problem statementManufacturing and materials

AI AR assisted comprehensive visual inspection of TPE331-12B engine of HTT-40 trainer aircraft

Source

Source summary

Use AI/AR assistance for visual inspection of the TPE331-12B engine used in HTT-40 trainers at induction and dispatch. The source identifies manual equipment-checklist work, accessory serial-number capture, missed defects or foreign objects, inconsistent inspections and poor coordination between stages. It requests support for this specific workflow rather than generic aircraft maintenance software.

No. 402Problem statementManufacturing and materials

Development of automation and data capturing in a quality control lab

Automate MIDHANI quality-control workflows for varied alloys and product forms, spanning sample collection/preparation, identification, testing and reports. The source emphasises many samples, multiple tests, long-term certification records and central processing of data from individual tests.

No. 420Problem statementManufacturing and materials

AI based condition monitoring system for yard assets

Use IoT measurements and machine learning to move shipyard equipment maintenance from periodic checks toward condition-based intervention. The requested output is actionable fault information from equipment-health data, rather than simply a sensor dashboard.

No. 709Problem statementManufacturing and materials

Design Development and Creation of a High-Fidelity Digital Twin Software Model for a Gas Turbine Starter Engine

Create a data-driven, high-fidelity software model of a gas-turbine starter engine. The source requests modular analytics, prognostics and visualisation, an explicit verification/validation framework, source code and documentation, training materials and phased customer-acceptance milestones with risk management.