Multi-Criteria Decision Making under Uncertainties in Composite Materials Selection and Design
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.
This work focuses 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. Using a Sparse Polynomial Chaos Expansion (SPCE) method, a data-driven model that establishes a relationship between input parameters and system objectives is constructed by analyzing data. 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.
D. Kumar et al., "Multi-Criteria Decision Making under Uncertainties in Composite Materials Selection and Design," Composite Structures, vol. 279, article no. 114680, Elsevier, Jan 2022.
The definitive version is available at https://doi.org/10.1016/j.compstruct.2021.114680
Nuclear Engineering and Radiation Science
Keywords and Phrases
Composite structures and heterogeneous materials; Multi-scale modeling; Robust optimization; Sparse Polynomial Chaos Expansion; Uncertainty analysis
International Standard Serial Number (ISSN)
Article - Journal
© 2022 Elsevier, All rights reserved.
01 Jan 2022
The authors would like to acknowledge the support of EU H2020 COMPOSELECTOR project (Grant No: 721105).