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<p>Flexible cellular structures with network-like architectures are promising for engineering applications, but their design requires balancing conflicting objectives, such as stiffness, energy absorption, and Poisson’s ratio. This study introduces a novel class of cross-linked cellular structures and investigates their mechanical performance using finite element analysis (FEA) combined with Taguchi design of experiments (DoE), analysis of variance (ANOVA), and Genetic Algorithm (GA)-based multi-objective optimization. Taguchi analysis identified cell orientation as the most influential factor, while GA optimization (population = 100, exchange rate = 0.8, mutation rate = 0.05, 200 generations) generated Pareto-optimal trade-offs between Young’s modulus (E), strain energy (U), and Poisson’s ratio (ν). Optimized structures achieved E ranging from 1300 to 1600&#xa0;MPa, strain energy improvements up to 28%, and ν reduced to 0.22 under targeted loading conditions. Experimental validation confirmed close agreement with simulations. The proposed hybrid Taguchi–GA framework provides a computationally efficient and robust pathway for designing functionally graded cellular structures with tailored mechanical properties, suitable for lightweight and energy-absorbing applications.</p>

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Multi-objective Optimization of Novel Cellular Structures for Crashworthiness and Lightweight Applications

  • 期刊论文

  • 发表日期：

    2026年2月5日

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  • Yazid Ait Ferhat,
  • Houssem Eddine Fiala,
  • Toufik Benmansour,
  • Mohammed Mousaab Blaoui,
  • Yasmine Boudjaada,
  • Tarek Bouakba,
  • Abdellah Reguieg Yssaad,
  • Younes Djemaoune

摘要

Flexible cellular structures with network-like architectures are promising for engineering applications, but their design requires balancing conflicting objectives, such as stiffness, energy absorption, and Poisson’s ratio. This study introduces a novel class of cross-linked cellular structures and investigates their mechanical performance using finite element analysis (FEA) combined with Taguchi design of experiments (DoE), analysis of variance (ANOVA), and Genetic Algorithm (GA)-based multi-objective optimization. Taguchi analysis identified cell orientation as the most influential factor, while GA optimization (population = 100, exchange rate = 0.8, mutation rate = 0.05, 200 generations) generated Pareto-optimal trade-offs between Young’s modulus (E), strain energy (U), and Poisson’s ratio (ν). Optimized structures achieved E ranging from 1300 to 1600 MPa, strain energy improvements up to 28%, and ν reduced to 0.22 under targeted loading conditions. Experimental validation confirmed close agreement with simulations. The proposed hybrid Taguchi–GA framework provides a computationally efficient and robust pathway for designing functionally graded cellular structures with tailored mechanical properties, suitable for lightweight and energy-absorbing applications.