Elaboration of a Multi-Objective Optimization Method for High-Speed Train Floors Using Composite Sandwich Structures

The transportation industry needs lightweight structures to meet economic and environmental demands. Composite sandwich structures offer high stiffness and low mass, making them ideal for weight reduction in high-speed trains. The objective of this research is to develop a method of weight and cost optimization for floors of high-speed trains. The studied sandwich floor structure consists of Fiber Metal Laminates (FML) face sheets and a honeycomb core. Different variations of FMLs were investigated to define the optimal sandwich structure for minimum weight and cost. The Neighborhood Cultivation Genetic Algorithm (NCGA) was used to search the design space, and the Finite Element Method (FEM) was used to construct the optimal design of the train car floor panel. The FEM and optimization results had a maximum difference about 11%. The study concluded that using face sheets made entirely of Fiber-Reinforced Plastic (FRP) or Fiber Metal Laminates (FMLs) resulted in significant weight savings of approximately 62% and 32%, respectively, compared to a sandwich structure made entirely of aluminum, but a lighter structure was associated with higher cost. The main contribution of this study is the elaboration of a multi-objective optimization method that utilizes a wide range of constituent materials and structural components in order to construct weight- and cost-optimized sandwich structures.

Automated summary

The transportation industry requires lightweight structures for economic and environmental reasons, and composite sandwich structures are ideal for weight reduction in high-speed trains due to their high stiffness and low mass. This research aims to develop a weight and cost optimization method for high-speed train floors using Fiber Metal Laminates (FML) face sheets and a honeycomb core. Different variations of FMLs were studied to determine the optimal sandwich structure with minimum weight and cost.