Surrogate-based global optimization of composite material parts under dynamic loading

Abstract

The design optimization of laminated composite structures is of relevance in automobile, naval, aerospace, construction and energy industry. While several optimization methods have been applied in the design of laminated composites, the majority of those methods are only applicable to linear or simplified nonlinear models that are unable to capture multi-body contact. Furthermore, approaches that consider composite failure still remain scarce. This work presents an optimization approach based on design and analysis of computer experiments (DACE) in which smart sampling and continuous metamodel enhancement drive the design process towards a global optimum. Kriging metamodel is used in the optimization algorithm. This metamodel enables the definition of an expected improvement function that is maximized at each iteration in order to locate new designs to update the metamodel and find optimal designs. This work uses explicit finite element analysis to study the crash behavior of composite parts that is available in the commercial code LS-DYNA. The optimization algorithm is implemented in MATLAB. Single and multi-objective optimization problems are solved in this work. The design variables considered in the optimization include the orientation of the plies as well as the size of zones that control the collapse of the composite parts. For the ease of manufacturing, the fiber orientation is defined as a discrete variable. Objective functions such as penetration, maximum displacement and maximum acceleration are defined in the optimization problems. Constraints are included in the optimization problem to guarantee the feasibility of the solutions provided by the optimization algorithm. The results of this study show that despite the brittle behavior of composite parts, they can be optimized to resist and absorb impact. In the case of single objective problems, the algorithm is able to find the global solution. When working with multi-objective problems, an enhanced Pareto is provided by the algorithm.