A COMPREHENSIVE INTERDISCIPLINARY STUDY ON BUCKLING BEHAVIOR OF AUXETIC HONEYCOMB SANDWICH NANOBEAMS: INTEGRATING NONLOCAL ELASTICITY, GENETIC ALGORITHM OPTIMIZATION, AND TECHNOLOGY FORESIGHT

This interdisciplinary study presents a comprehensive investigation into the buckling behavior of sandwich nanobeams with auxetic honeycomb cores, integrating nonlocal elasticity theory, genetic algorithm (GA)-based optimization, and technology foresight analysis. A third-order shear deformation theory (TSDT) combined with Eringen's nonlocal elasticity captures size-dependent effects critical for nano-electromechanical systems (NEMS). The effective mechanical properties of the re-entrant hexagonal honeycomb core are calculated using the standard Gibson-Ashby formulations for cellular solids. A novel contribution is the implementation of a genetic algorithm (GA) as an AI-based optimization tool, which efficiently identifies optimal cell geometry parameters (۰ = ۴۴.۶°,h/l = ۱.۰۵,t/l = ۰.۲۰۰,hc/h = ۰.۴۰) achieving ۲۲.۵۴% higher buckling load compared to conventional cores significantly surpassing the ۱۱.۴۰% improvement from traditional grid search methods, with ۷۳% less computational cost and ۴۲% better robustness. Furthermore, a technology foresight analysis projects the evolution of auxetic nanomaterials over the next decade (۲۰۲۶-۲۰۳۶), identifying key drivers including advances in ۳D nanoprinting, AI-driven materials discovery, and sustainable development goals. The results confirm that the synergy between nanotechnology, AI-based optimization, and strategic foresight can accelerate the development of next-generation smart materials for aerospace, biomedical, and sustainable engineering applications. This work exemplifies how the convergence of nanotechnology, artificial intelligence, and strategic foresight can address Grand Challenges in advanced manufacturing.

کلیدواژه ها:

Sandwich Nanobeam ، Auxetic Core ، Nonlocal Elasticity ، Genetic Algorithm Optimization ، Buckling Analysis