A novel post‐forming method for fully cured thermosetting composite parts: Prediction and investigation

Author:

Sun Lishuai1ORCID,Xie Chenyang1,Liu Chuang1,Wang Junbiao1

Affiliation:

1. School of Mechanical Engineering Northwestern Polytechnical University Xi'an China

Abstract

AbstractThe objective of this work is to devise a post‐forming technique capable of accurately and significantly altering the shape of fully cured thermosetting composite parts. To accomplish this goal, the viscoelastic behavior and resulting deformation of fully cured parts at elevated temperatures were predicted and examined using viscoelastic mechanics, finite element analysis (FEA), surrogate modeling, and the particle swarm optimization (PSO) method. Firstly, the mechanism of fully cured parts' shape adjustment based on viscoelastic mechanics was introduced, and a method was established for the inverse identification of the stress relaxation ratio in the different directions of material. Secondly, an FEA model for predicting post‐forming was developed based on the standard linear‐solid viscoelastic model. A dataset was then compiled using parameterized FEA to train a rapid prediction model for post‐forming. The influence of various forming and structural parameters on the post‐forming process was examined. The results indicate that layup has the most significant impact, followed by displacement and temperature. The radial basis function was selected to construct the precise surrogate model after comparing it with the Kriging model and the Response Surface Methodology (RSM) model. Ultimately, to achieve the desired shape after post‐forming, the PSO method can be employed to determine the ideal combination of forming parameters.Highlights The post‐forming method could adjust the shape of fully cured composites. The mechanism of post‐forming is based on viscoelastic behavior. An accurate FEA based on the standard linear‐solid model was established. The radial basis function was developed for quick and precise prediction. The particle swarm optimization could obtain the ideal forming parameters.

Funder

National Natural Science Foundation of China

Publisher

Wiley

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