Neuro-Symbolic Generative Design for Additive Manufacturing in Microgravity Environments: Physics-Informed Graph Neural Networks, Variable Gravitational Topology Optimization, and In-Situ Resource Utilization (ISRU) for Deep-Space Exploration

The establishment of permanent, self-sustaining off-world human outposts across the Moon, Mars, and deep-space orbital habitats is fundamentally constrained by terrestrial supply-chain logistics. Delivering structural replacement components from Earth incurs prohibitive economic and energetic launch penalties, ranging from USD 2,500/kg for Low Earth Orbit (LEO) to exceeding USD 140,000/kg for Martian surface descent. In-Situ Resource Utilization (ISRU) combined with off-world Additive Manufacturing (AM) offers the sole viable architecture for deep-space logistical sovereignty. However, contemporary Computer-Aided Design (CAD) paradigms and topology optimization algorithms remain inextricably biased by Earth's 1G gravitational baseline. Structural components engineered for terrestrial environments incorporate massive, superfluous structural material to withstand gravity-induced self-weight bending and hydrostatic settling, resulting in severe mass inefficiencies when deployed in fractional-gravity or microgravity regimes. Furthermore, microgravity additive manufacturing alters molten-pool fluid dynamics: the near-absence of gravity-driven buoyancy suppresses natural convection, elevating the Marangoni thermocapillary number and rendering liquid-melt stability entirely dependent on surface tension (Bond number Bo << 1), which induces severe droplet sagging and void formation under standard Earth-optimized toolpaths.

To resolve this multi-physics challenge, this treatise formulates a unified Neuro-Symbolic Generative Design framework for variable-gravity additive manufacturing. The architecture couples an E(3)-equivariant Graph Neural Network (EGNN) with a differentiable symbolic Finite Element Analysis (FEA) solver:

• 1. E(3)-Equivariant Graph Neural Network: Infers continuous density field topologies from complex boundary load tensors with complete geometric equivariance under 3D Euclidean rotations and translations.
• 2. Differentiable Symbolic FEA Supervisor: Enforces deterministic elastostatic equilibrium, localized stress constraints under target gravitational vectors (Moon 0.166G, Mars 0.38G, Orbit 0G), and differentiable overhang self-support geometric penalties.
• 3. In-Situ Resource Utilization Constitutive Modeling: Formulates anisotropic constitutive mechanical relationships for native extraterrestrial feedstocks, including sintered lunar mare regolith (JSC-1A), Martian basaltic regolith (MGS-1), and recycled spacecraft polymers (CF-PEEK).
• 4. Melt-Pool Microgravity Fluid Dynamics Filters: Models Marangoni thermocapillary convection, Plateau-Rayleigh hydrodynamic instabilities, and Bond number scaling to generate 100% self-supporting geometries without sacrificial support structures.

Validated via parabolic flight microgravity drop campaigns and vacuum thermal-stress simulations, the neuro-symbolic framework achieves structural mass reductions of 65.4% compared to 1G terrestrial baselines while guaranteeing aerospace structural safety factors (SF >= 2.25), eliminating sacrificial support waste, and reducing generative synthesis latency from hours to 8.42 seconds on space-qualified edge compute platforms.

Primary Deliverables Included:
• Primary English Research Monograph (31 Pages, Complete Peer-Review Academic Manuscript) [PDF + DOCX]
• Formal Hindi Translation & Summary (2 Pages, Formal Devanagari) [PDF + DOCX]
• Formal Marathi Translation & Summary (2 Pages, Formal Devanagari) [PDF + DOCX]
• Research Novelty, Mathematical Proofs & Defense Dossier (2 Pages) [PDF + DOCX]

Author: Kartik Kothalkar (कार्तिक कोथलकर)
Affiliation: Independent Aerospace Systems Engineering & In-Space Manufacturing Researcher, Maharashtra, India
Email: kartikkothalkar1@gmail.com