Multi-criteria decision making under uncertainties in composite materials selection and design

Abstract During a composite application’s initial design stages, the main objective is to have the optimal performance of the final structure. There is a vast demand for lightweight structures with minimum cost and enhanced safety features in all heavy-duty and performance-based industries such as aerospace, automobile, and sports. In order to make prudent decisions and establish the reliability of industrial decision-makers, it is paramount to consider the impacts of uncertainties on the strength and cost of the structure. For that reason, every source of uncertainty should be included when designing an optimal engineering device. In this work, we focus on applying multidisciplinary optimization tools for the optimal design of fiber-reinforced composites under uncertainties arising from different scales. For demonstration, we consider a composite leafspring for optimization under uncertainties. Material microstructure accounts for microscale uncertainties while composite layers stacking sequence and structural loading account for meso and macroscale uncertainties, respectively. Utilizing an adaptive Sparse Polynomial Chaos Expansion (SPCE) method, a data driven model that establishes a relationship between input parameters and system objectives by analysing data is constructed. Results are provided with respect to both variations and probability distributions. The stiffness and the cost of the leafspring are the design objectives. Finally, the robust optimal designs are discussed using the Pareto front.