跳到主要导航 跳到搜索 跳到主要内容

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

  • Feng Song
  • , Jiachen Zhang
  • , Bingyi Lyu
  • , Shiyu Wang
  • , Jinyou Xiao
  • , Lihua Wen
  • , Xiao Hou
  • Northwestern Polytechnical University Xian
  • China Aerospace Science and Technology Corporation

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

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%.

投稿的翻译标题Influence of multiple process parameters on the friction coefficient of prepregs and machine learning prediction method
源语言繁体中文
页(从-至)5801-5811
页数11
期刊Fuhe Cailiao Xuebao/Acta Materiae Compositae Sinica
41
11
DOI
出版状态已出版 - 11月 2024
已对外发布

关键词

  • carbon fiber prepreg
  • friction
  • machine learning
  • multiple processing parameters
  • performance prediction

学术指纹

探究 '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' 的科研主题。它们共同构成独一无二的学术指纹。

引用此