Bionics structural modification and collaborative optimization design of a waste crossbeam based on an RBF neural network and NSGA-II

In this work, a bionic structure was redesigned for a specific type of crossbeam and the corresponding modification strategies were formulated. Three modification schemes were put forward and the corresponding parameterized analysis models established. Sensitivity analysis results guided the variable parameter selection and the total design of an orthogonal test. Data obtained were utilized to establish a neural network model that reflects the dynamic nonlinear mapping relation. Then, a collaborative optimization design method was proposed that was based on the neural network and the genetic algorithm for secondary design of the rib thickness. Through optimizing the neural network model by NSGA-II, Pareto optimal results were obtained. The results showed that the mass of the newly designed crossbeam had been reduced by 3.74%, while the first three orders of natural frequency had been increased by 6.67%, 3.47% and 14.20%, respectively. The bionic modification structure and the collaborative optimization design method were proved to be valuable for redesign in the remanufacturing field.