Identification of material properties of green laminate composite plates using bio-inspired optimization algorithms

Abstract

This work proposes a non-destructive method for the identification of material properties of composite materials. The proposed optimization problems have for design variables the material elastic constants and make use of nature-inspired metaheuristic optimization algorithms. The objective functions relate experimental natural frequencies with computationally obtained ones. The nature-inspired metaheuristic optimization algorithms used are the: (1) Genetic algorithm, (2) Particle Swarm Optimization algorithm, (3) Grey Wolf Optimization algorithm, (4) Firefly algorithm, and (5) Cuckoo Search algorithm. The study is focused on laminated composite materials, whether they are synthetic fiber reinforced, such as glass fibers reinforced composites, or natural fibers reinforced like wooden fibers reinforced composites and plywood. The proposed method allows the identification of the elastic constants within an acceptable range compared to other methods, provided that enough natural frequencies are accurately measured. This method presents several advantages in comparison to other methods: (1) it does not require an initial guess of the elastic constants, (2) it does not need the gradient of the objective functions, and (3) it allows the identification of a large range of elastic constants of different materials due to its good adaptability and versatility.