Topology Optimization of a Load Cell via a Multi-objective Genetic Algorithm

A load cell for measuring the lift force generated by flapping wings of an insect must have a high fundamental frequency and low stiffness to meet the stringent precision requirements. This thesis aims to design a load cell that can record the waveform of the lift of an insect accurately. Starting from a rectangular shape with specified material properties, a multi-objective genetic algorithm, called NSGA-II, is employed to find the optimal shape of the load cell. A finite element analysis program is developed to determine the values of the objective functions. In NSGA-II, the non-dominated sorting method and crowded-comparison approach are used to increase the genetic diversity as well as keep the elite genes. Because the boundary conditions and applied force are symmetric with respect to the central line of the load cell, we restrict the outcomes of the optimization program to symmetrical structures. The restriction is then removed. The performance of the asymmetrical optimal results thus generated is compared with that of the performance of the symmetrical ones. In order to reduce the computation time, the optimization is first performed on a coarse mesh for the generation of primitive structures. Then the meshes of some specified areas of a primitive optimal structure are refined. Optimization is performed on the refined meshes to determine the final topology of the load cell. In this way, the computational burden can be largely reduced. Prototypes of several optimal designs are manufactured by 3D printing. Experimental test results for the fundamental frequency and flexibility of these prototypes are compared with those of the numerical simulation.