多工艺参数对预浸料摩擦系数的影响及机器学习预示方法

Translated title of the contribution: Influence of multiple process parameters on the friction coefficient of prepregs and machine learning prediction method

Feng Song, Jiachen Zhang, Bingyi Lyu, Shiyu Wang, Jinyou Xiao, Lihua Wen, Xiao Hou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

During the forming process of composites, the friction-sliding behavior between prepreg ply-ply and ply-tool may lead to defects such as wrinkles and pores, which seriously affect the mechanical properties of the components. However, there are many factors affecting the inter-ply friction of the prepreg plies in the forming process of complex components. The existing theoretical models contain insufficient process parameters, resulting in the accuracy of forming process simulation not meeting high-quality forming requirements. In this paper, a friction test method for carbon fiber prepregs was designed for multiple process parameters. The influence of sliding velocity, normal force, viscosity, surface roughness, contact material, and fiber orientation on the friction coefficient were studied. Taking the typical fiber orientations of 0o/45o/90o as examples, the inter-ply friction mechanism in different fiber orientations was revealed. In order to predict the friction coefficient of prepreg corresponding to multiple process parameters rapidly and accurately, a prediction model for the friction coefficient of prepreg was established using the support vector regression (SVR) method. Taking the prepreg ply-ply friction behavior with relative fiber orientation of [30o/0o] and [60o/0o] as examples, the experiments and predictions were conducted, and the error was less than 9%.

Translated title of the contributionInfluence of multiple process parameters on the friction coefficient of prepregs and machine learning prediction method
Original languageChinese (Traditional)
Pages (from-to)5801-5811
Number of pages11
JournalFuhe Cailiao Xuebao/Acta Materiae Compositae Sinica
Volume41
Issue number11
DOIs
StatePublished - Nov 2024
Externally publishedYes

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