Finite element model correction method based on surrogate model with multiple working conditions and multiple measurement points

Abstract

The finite element model inversion method has been widely used in recent years for iterative adjustment of finite element model parameters. However, the models constructed in the existing literature are weak and time consuming to adapt to the environment, which makes it difficult to adapt to the current needs of numerical simulations. To address the problem of large uncertainty in the material parameters of real objects and the difficulty of constructing finite element simulation models, a surrogate-based model correction method was proposed for multi-condition and multi-measurement point finite element models. The innovative use of the working condition parameter as one of the training parameters of the surrogate model to construct the optimal mathematical model for parameter correction of the finite element model to variable working conditions. To reduce the number of finite element model calls and speed up the convergence process, an MP-CV-Voronoi parallel infill sampling method for the surrogate model was proposed to overcome the problems of easily falling into local optima and slow convergence when solving after constructing the surrogate model. The proposed parallel infill sampling method was tested using the test functions. The finite element model correction method with multiple working conditions and multiple measurement points was applied for material parameter correction and identification of aluminum alloys. The superiority of the proposed parallel point addition method in terms of the solution accuracy and speed was demonstrated. The results show that the multi-measurement points have a significant effect on improving the model correction effect, and the constructed multi-condition surrogate model can make fast predictions for arbitrary conditions and has strong environmental adaptability. The finite element model correction method proposed in this paper, with strong environmental adaptability, high accuracy and fast iteration, has been tested to be very effective.

Special features on continuous learning based condition monitoring for crucial components

“…A surrogate-based model correction method was proposed for multi-condition and multi-measurement point finite element models to address the large uncertainty issue in the material parameters of real objects and the difficulty of constructing finite element simulation models. This method displays strong environmental adaptability, high accuracy, and fast iteration [2].…”

“…A surrogate-based model correction method was proposed for multi-condition and multi-measurement point finite element models to address the large uncertainty issue in the material parameters of real objects and the difficulty of constructing finite element simulation models. This method displays strong environmental adaptability, high accuracy, and fast iteration [2].Condition monitoring is an important way to understand and master the running state of the equipment, which is indispensable in industrial production. Article [3] proposed a health indicator (HI) construction method based on statistical learning modeling for machine condition monitoring.…”