Machine Learning-based Prediction of Impact Response in Kevlar<sup>®</sup> Yarn with 3D Visualization

HU Yiting; LIU Wanli; YUAN Zishun; GU Bingfei; XU Wang

doi:10.12382/bgxb.2025.0492

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Machine Learning-based Prediction of Impact Response in Kevlar^® Yarn with 3D Visualization

更新时间：2026-04-24

- Machine Learning-based Prediction of Impact Response in Kevlar^® Yarn with 3D Visualization
- Acta Armamentarii Vol. 47, Issue 3, Pages: 250492(2026)
- 作者机构：
  
  浙江理工大学服装学院，浙江杭州 310018
  浙江理工大学浙江省服装工程技术研究中心，浙江杭州 310018
  浙江理工大学丝绸文化传承与产品设计数字化技术文化和旅游部重点实验室，浙江杭州 310018
  北卡罗来纳州立大学威尔逊纺织学院，美国北卡罗来纳州罗利 27695
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0492
  CLC： TH145.2;TP181
- Received：12 June 2025，
  
  Online First：25 December 2025，
  
  Published：2026-03
- 稿件说明：
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HU Yiting, LIU Wanli, YUAN Zishun, et al. Machine Learning-based Prediction of Impact Response in Kevlar^® Yarn with 3D Visualization[J]. Acta Armamentarii, 2026, 47(3): 250492. DOI: 10.12382/bgxb.2025.0492.
DOI：

HU Yiting, LIU Wanli, YUAN Zishun, et al. Machine Learning-based Prediction of Impact Response in Kevlar^® Yarn with 3D Visualization[J]. Acta Armamentarii, 2026, 47(3): 250492. DOI: 10.12382/bgxb.2025.0492. DOI：

摘要

有限元方法在微观和介观尺度下预测高速冲击中高性能纱线的力学响应时，网格尺寸小、单元数量多，计算过程耗时长，且现有机器学习方法在三维结构响应的直接预测方面仍存在局限。为解决上述问题，提出基于前馈神经网络（Feedforward Neural Network，FNN）的机器学习预测与三维可视化方法。以凯夫拉纱线为研究对象，构建基于有限元数据驱动的FNN模型，将材料参数与冲击时间点作为输入，预测出纱线节点坐标和单元von Mises应力。并结合三维可视化技术，实现对纱线冲击响应的直观呈现。对比分析结果表明：所提方法生成的纱线三维模型在形变与应力分布方面与有限元仿真结果高度相似，且从参数输入到结果输出的全过程耗时约3s，计算效率较FEM提升近10倍；该方法为高性能防护材料的冲击响应预测提供了一种高效手段。

Abstract

The finite element method（FEM）incurs a substantial computational cost due to small mesh sizes and large element counts when predicting the mechanical response of high-performance yarn under high-velocity impact at micro-scales and meso-scales. Meanwhile

the existing machine-learning methods still have limitations in the direct prediction of three-dimensional structural response. To address these issues

a FNN-based machine-learning prediction and three-dimensional visualization method is proposed. A FEM-data-driven FNN model is constructed for Kevlar

yarn

which takes the material parameters and impact time points as inputs to predict the coordinates of yarn node and the von Mises stress of elements. Coupled with three-dimensional visualization

the method provides an intuitive depiction of the impact response. Comparative analysis shows that the three-dimensional yarn mod

el generated by the proposed method is highly similar to FEM simulation results in terms of deformation and stress distribution

and the end-to-end runtime from parameter input to result output is approximately 3 seconds

yielding nearly a tenfold improvement in computational efficiency over FEM. The proposed method offers an efficient means for predicting the impact response of high-performance protective materials.

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references

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Machine Learning-based Prediction of Impact Response in Kevlar® Yarn with 3D Visualization

DOI：10.12382/bgxb.2025.0492

摘要

Abstract

关键词

Keywords

references

Machine Learning-based Prediction of Impact Response in Kevlar^® Yarn with 3D Visualization