Time to move this thread to Indian section
My basic understanding says, Govt of India will prioritize 6th gen technology more than 5th gen aircraft. While American have been operating it for the past 30 years, the Chinese and Russians for last decade, the Russians have develop strategy to counter them with more lethal air defence, which has been observed in west Asia conflict. In coming 10 years there will be counter to 5th Gen technology too.
When DRDO has made RAM, RAS, etc, working on DEW, TVC, etc then we don't need full airframe of FCAS/GCAP, just the engine & some components.
I don't think the manufacturing of 6th gen airframe will follow same lean manufacturing technology as 5th generation. A lot of manufacturing deficiency detection will be machine learning based. India will have to outsource powerful tools and semiconductors for this. As you already mentioned
- Generative design: AI algorithms explore thousands of component geometries, balancing weight, strength, and aerodynamics faster than conventional methods. For example, Boeing has patented software tools to optimize structural part profiles and uses AI-driven simulations to validate designs more efficiently, thereby supporting the development of lighter, stronger components.
- Intelligent robotics: AI-driven robots handle precision tasks such as drilling, painting, and assembly, thereby reducing errors and cycle times. Companies such as Airbus employ intelligent robotics to automate complex assembly lines and enhance quality control in aircraft manufacturing. For instance, at its Hamburg facility, Airbus has implemented advanced robotic systems for structural assembly, including seven-axis robots for precise drilling and Flextrack robots that move along rails installed on the fuselage. These robots contribute to improved precision, reduced errors, and enhanced efficiency in the assembly process. Similar automation breakthroughs can be observed in AI-driven 3D printing, where machine learning models optimize build parameters to improve quality consistency in additive manufacturing.
- Material discovery: ML analyzes material datasets to identify alloys and composites with superior performance for aerospace components.
- Design speed: Predictive analytics and physics-aware AI models enable rapid design iteration, addressing technical issues such as fuel optimization. An AI system that integrates machine learning, data management, and simulation enables real-world aerospace applications by supporting continuous testing and environment modeling. Collaborations like Neural Concept and Airbus have reduced prediction time from hours to milliseconds.
AI in Aerospace Engineering: Redefining Intelligent Design | Neural Concept
Discover the transformative impact of AI in aerospace design, reshaping defense strategies and intelligent engineering. Learn more at NC.
23 IITs with 18,000+ seats.
Only the old IITs are credible IITs. The new ones aren't really ready, infact NIT Pryagraj is better than most of the new IITs in research and learning.
