Bharat in Betastartup field guide
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No. 402Problem statementManufacturing and materials

Development of automation and data capturing in a quality control lab

Source

Source summary

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.

No. 721Problem statementManufacturing and materials

Automation of I-level Test bench of Inertial Navigation System (G3INS)

Automate intermediate-level testing of the G3INS inertial-navigation system. The current bench requires multiple schedules and operator-issued commands, and the source reports approximately 150 hours to complete acceptance testing of one unit. The target is reduced manual involvement in the existing test workflow, not an unspecified navigation product.