Supervised learning-guided design and CNC fabrication of Schwarz TPMS with experimental evaluation for atmospheric water generation

Atmospheric Water Generation (AWG) offers a decentralized and sustainable solution to global water scarcity by extracting moisture from the air. This study enhances AWG performance by combining supervised learning-guideddesign optimization and experimental validation of Schwarz Triply Periodic Minimal Surface (TPMS) structures. TPMS geometries improve condensation through a combination of large surface area and enhanced thermal conduction, enabling efficient droplet shedding. Structural and thermal simulations in nTop software generated data for predicting the top surface temperature-critical for condensation-based on parameters such as lattice thickness, cell size, surface area, and solid volume. Among the supervised learning models tested (Support Vector Machine, K-Nearest Neighbor, and Decision Tree), the Decision Tree showed the best accuracy (R² = 0.9676), captuirng both linear and nonlinear relationships. Using optimized parameters, a Schwarz TPMS structure was fabricated from Aluminum 6061-T6 via multi-axis CNC milling through a layer-wise subtractive method. Experimental evaluation under thermoelectric cooling (4-7 V) showed that the TPMS outperformed a solid aluminum block at 5 V and 6 V, achieving up to 33% higher water yield, though performance declined at 7 V due to frost formation. Thermal mapping highlighted non-uniform cooling from interfacial resistance between layers. This work demonstrates a scalable approach that integrates supervised learning-guided design with subtractive manufacturing for efficient AWG systems.