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March 2, 20260 citationsOpen Access

Generative Design and AI Driven Topology Optimization for Sustainable Aerospace Structures

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STSophia Chen, Marcus Thorne, Yuki Tanaka

Key Points

  • The aim is to explore how generative design and AI-driven topology optimization can reduce the weight of aerospace structures while ensuring safety.
  • Applied generative design algorithms and topology optimization to aircraft components.
  • Utilized physics-informed neural networks (PINNs) for analysis.
  • Compared traditional manufacturing methods to the new AI-driven approaches.
  • Achieved a 45% reduction in component weight for load-bearing brackets and engine mounts.
  • Maintained structural integrity and fatigue life in compliance with 2026 FAA safety standards.
  • Provided a framework for the development of ultra-light aircraft.

Abstract

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.

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Cite This Study

Sophia Chen, Marcus Thorne, Yuki Tanaka (2026) studied this question.

synapsesocial.com/papers/69a52e04f1e85e5c73bf1439https://doi.org/10.5281/zenodo.18814260
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