Multi-objective ANN optimization of bio char-enhanced epoxy–glass–basalt hybrid composites for advanced UAV and aerospace thermo-structural applications

Hybrid composite systems utilizing biochar from agricultural residues have garnered significant interest in aerospace engineering, particularly for unmanned aerial vehicles (UAVs) and thermally stressed structures. This study investigates the thermo-mechanical properties, environmental durability, and structural stability of epoxy–glass–basalt hybrid laminates reinforced with biochar at loadings ranging from 0 to 4 wt.%. A series of standardized tests were conducted to evaluate the composites’ performance, including tensile, flexural, compressive, impact, and flammability assessments. The findings displayed non-linear trends in mechanical and functional characteristics, with the 3 wt.% biochar laminate (GB3) showing the best balance in performance—notably achieving a tensile strength of 217.07 MPa and an impact energy of 5.5 J. Additionally, GB3 recorded the highest hardness at 81.2 Shore D and minimized water absorption to 3.67%. Microstructural analysis indicated optimal particle dispersion and adhesion in the GB3 laminate, contrasted by agglomeration issues in the 4 wt.% composition. To aid in material optimization, a multi-objective Artificial Neural Network (ANN) model was developed based on the experimental results, suggesting that approximately 3.13 wt.% biochar yields optimal composite performance with a desirability score of 0.5664. This composition maintained excellent impact resistance and low thermal conductivity while ensuring sufficient mechanical strengths. The study successfully merges experimental and computational methodologies to facilitate the design of biochar-enhanced composites tailored for critical aerospace applications. The developed composite demonstrates potential for lightweight UAV and aerospace-related applications; however, further high-temperature, fatigue and environmental testing is required before application-specific qualification.