Quality prediction for rocket body structure manufacturing process based on improved ELM

Abstract

In the current multi-variety and small-batch production mode, this paper proposes a quality prediction method for rocket body structure manufacturing process based on improved extreme learning machine (ELM). Aiming at the non-optimal problems caused by random selection of input layer weights and hidden layer bias of ELM, particle swarm optimization (PSO) is combined with gravitational search algorithm (GSA) to form a hybrid algorithm to optimize the random parameters of ELM. Taking a weld in a rocket body structure as an example, the weld width prediction model based on improved ELM is established and verified. The results show that compared with the prediction model based on the standard ELM and the prediction model based on PSO-ELM, the prediction model based on improved ELM has higher prediction accuracy, and its prediction deviation does not exceed 2.5%, which can effectively predict the weld width.