Generative Design and AI Driven Topology Optimization for Sustainable Aerospace Structures

The aerospace industry is undergoing a structural revolution driven by the integration of Artificial Intelligence (AI) and Additive Manufacturing (AM). Traditional subtractive manufacturing often results in "over-engineered" components that carry unnecessary weight. This paper explores the application of Generative Design algorithms and Topology Optimization (TO) to radically reduce the mass of load-bearing aircraft brackets and engine mounts. By utilizing Physics-Informed Neural Networks (PINNs), we demonstrate a 45% reduction in component weight while maintaining the structural integrity and fatigue life required by 2026 FAA safety standards. The study further analyzes the transition from "Design-for-Manufacturing" to "Design-for-Performance," highlighting how AI can synthesize complex bio-mimetic geometries that were previously impossible to produce. Our results provide a framework for the next generation of "Ultra-Light" aircraft, providing a technical path to carbon-neutral aviation.