Design, manufacturing, and structural optimization of a composite float using particle swarm optimization and genetic algorithm

Abstract

In the realm of laboratory experiments dealing with solutions, float is a dynamic part of the testing device to help monitor and measure the level change of the solution, which has a significant influence on the reliability of the data recorded. Thus, the material selection, design, and structural configuration of the float need to be optimized based on the test exposure conditions, temperature, test requirements, and setup limitations, while maintaining the manufacturing to be simple and cost-effective. In the present study, a composite float design, manufacturing, and optimization have been undertaken based on the performance constraints and setup requirements. For this purpose, readily available composites were taken into account in terms of density, temperature resistance, and alkali resistance, resulting in two design alternatives such as polyvinyl chloride and low-density polyethylene. Then, the structural configurations were considered and a performance-based design was implemented. In order to minimize the length of the composite float to make it fit well into the test setup, the design problem was formulated into a constrained optimization problem with four design scenarios and optimization was conducted using genetic algorithm and particle swarm optimization. The results obtained showed that the solutions of genetic algorithm and particle swarm optimization were comparable; however, those of particle swarm optimization proved to be more accurate in case of more complex design scenario, and also faster by two to three times. The manufacturing was conducted through plastic welding and heat shrink tubing and final assembly of linear variable differential transformer and steel weight. It was found that the composite float has a pretty sensitive and accurate performance and can reliably fit and be used in the experimental setup.