连续纤维增强复合材料增材制造预测建模研究进展-强度所60周年专刊

  • 韩智 ,
  • 王玉思 ,
  • 张文瑶 ,
  • 李冰 ,
  • 陈园
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  • 1. 南方科技大学
    2. 西北工业大学航空学院

收稿日期: 2025-05-28

  修回日期: 2025-07-29

  网络出版日期: 2025-07-30

基金资助

国家自然科学基金青年科学基金项目;深圳市自然科学基金面上项目;深圳市连续碳纤维复合材料智能制造重点实验室;强度与结构完整性全国重点实验室开放基金;广东省普通高校重点领域专项(高端装备制造)

Progress in Modeling of FFF for Continuous Fiber-Reinforced Composites

  • HAN Zhi ,
  • WANG Yu-Si ,
  • ZHANG Wen-Yao ,
  • LI Bing ,
  • CHEN Yuan
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Received date: 2025-05-28

  Revised date: 2025-07-29

  Online published: 2025-07-30

摘要

连续纤维增强复合材料(Continuous Fiber Reinforced Composite, CFRC)以其优异的力学性能而被广泛应用于航空航天、轨道交通等高端装备领域。近年来,增材制造(即3D打印)技术突破了传统制造工艺对模具需求的束缚而逐步成为CFRC制造的主要手段之一。本综述系统总结了CFRC增材制造预测建模的最新研究进展,全面归纳了从增材制造成型过程到3D打印结构力学建模的主要技术发展路径。在CFRC增材制造成型建模方面,介绍了CFRC的树脂流动与浸润行为、热传导机理、残余应力演变及纤维错位控制等方面的研究成果;在3D打印CFRC结构力学建模方面,分别从微观、介观和宏观尺度阐述了主流的建模方法及应用场景,并探讨了多尺度建模方法及其发展潜力。最后,针对当前CFRC增材制造预测建模中存在的关键问题,本文系统探讨了现今主要挑战和未来发展方向,为高性能CFRC增材制造的科学研究和工程应用提供了理论指导和技术参考。

本文引用格式

韩智 , 王玉思 , 张文瑶 , 李冰 , 陈园 . 连续纤维增强复合材料增材制造预测建模研究进展-强度所60周年专刊[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2025.32311

Abstract

Continuous Fiber Reinforced Composites (CFRCs) are ex-tensively employed in advanced industries such as aero-space and rail transit, owing to their superior mechanical properties. In recent years, additive manufacturing (3D printing) has emerged as a primary method for fabricating CFRCs, eliminating the limitations of traditional manufac-turing processes that rely on molds. This review provides a comprehensive overview of the latest advancements in pre-dictive modeling of CFRCs additive manufacturing, cover-ing the entire technological development pathway from process modeling to structural mechanics modeling of 3D printed CFRCs. The section on process modeling of CFRCs additive manufacturing explores key aspects such as resin flow and infiltration behavior, heat transfer mechanisms, residual stress evolution, and fiber misalignment control. The subsequent section on structural mechanics modeling of 3D printed CFRCs presents mainstream modeling approach-es at the micro, meso, and macro scales, highlighting their application scenarios. It also examines the potential of mul-tiscale modeling techniques, which bridge these different scales to enhance predictive accuracy. Finally, this review systematically identifies the major challenges currently faced in predictive modeling of CFRCs additive manufac-turing and outlines future research directions. These insights provide theoretical guidance and technical support for ad-vancing the scientific understanding and practical applica-tion of high-performance CFRCs in additive manufacturing.
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