An Efficient Method for the Inverse Design of Thin-Wall Stiffened Structure Based on the Machine Learning Technique

Abstract

In this paper, a new method using the backpropagation (BP) neural network combined with the improved genetic algorithm (GA) is proposed for the inverse design of thin-walled reinforced structures. The BP neural network model is used to establish the mapping relationship between the input parameters (reinforcement type, rib height, rib width, skin thickness and rib number) and the output parameters (structural buckling load). A genetic algorithm is added to obtain the inversely designed result of a thin-wall stiffened structure according to the actual demand. In the end, according to the geometric parameters of inverse design, the thin-walled stiffened structure is reconstructed geometrically, and the numerical solutions of finite element calculation are compared with the target values of actual demand. The results show that the maximal inversely designed error is within 5.1%, which implies that the inverse design method of structural geometric parameters based on the machine learning and genetic algorithm is efficient and feasible.