An adaptive hybrid surrogate model for FEA of telescopic boom of rock drilling jumbo

Abstract

The rapid collaborative optimization (CO) has an increasing demand for high-fidelity surrogate models. However, the traditional surrogate model cannot be applied to all working conditions due to the limitations of model applicability. A hybrid surrogate model is proposed, which uses the attention mechanism to automatically decide the weights of the sub models according to the working conditions to ensure its approximation ability under all working conditions. First, according to the characteristics of the finite element analysis (FEA) parameters, a comprehensive design of experiment (DOE) is proposed, which ensures the space-filling property of the samples. Secondly, a Self-attention artificial neural network (ANN) is proposed to automatically adjust the weights of sub-surrogate models, improving the attention to the working condition-related features. The proposed Self-attention ANN is a general framework that can provide support for the adaptive weight decision in other equipment simulation hybrid surrogate models. The experiment on the database shows that the error of the two hybrid surrogate models established by the proposed method is 36.04% and 33.31% lower than that of the advanced model, respectively, and is significantly superior to other methods. This achievement not only combines the spatial approximation ability of sub models to establish nonlinear model, achieving the purpose of high-fidelity simulation of FEA systems, but also enables surrogate model covering complete space using limited samples, making the model suitable for various practical engineering problems. In summary, the proposed method has obvious advantages in solving existing problems, provides strong support for research and practical applications.