A Multi-objective Optimization Approach for Identifying 3D Elastic Behavior in Elium®/Flax Fiber Biocomposites

This chapter delves into the dynamic behavior of Elium®/Flax fiber biocomposites, focusing on the identification of their 3D elastic constants. The study employs a multi-objective optimization approach, combining experimental frequency measurements, finite element simulations, and optimization techniques. Key topics include the use of Design of Experiments (DoE) and Response Surface Methodology (RSM) to model the relationships between engineering constants and natural frequencies, as well as the application of Non-Dominated Sorting Genetic Algorithms (NSGA-II and NSGA-III) for optimization. The study also explores the influence of population and generation sizes on the convergence of numerical frequencies toward experimental results. The findings highlight the superior accuracy of the synergistic RSM model and the efficiency of NSGA-II in minimizing the Mean Squared Error (MSE) between experimental and numerical frequencies. The chapter concludes with a comparative analysis of NSGA-II and NSGA-III, demonstrating the former's efficiency in terms of computation time and accuracy. This research provides valuable insights into the dynamic behavior of biocomposites, offering a robust framework for future studies in this field.