Damage Quantification Assessment of Ultra-high Molecular Weight Polyethylene Based on Multiple Constraints

ZHANG Fubao; CHEN Hongyan; ZHOU Lan; LIU Susu

doi:10.12382/bgxb.2025.0790

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Damage Quantification Assessment of Ultra-high Molecular Weight Polyethylene Based on Multiple Constraints

更新时间：2026-05-06

- Damage Quantification Assessment of Ultra-high Molecular Weight Polyethylene Based on Multiple Constraints
- Acta Armamentarii Vol. 47, Issue 4, Pages: 250790(2026)
- 作者机构：
  
  南通大学机械工程学院，江苏南通 226019
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0790
  CLC： TQ325.1+2;TP391.4
- Received：01 September 2025，
  
  Online First：27 January 2026，
  
  Published：2026-04
- 稿件说明：
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ZHANG Fubao, CHEN Hongyan, ZHOU Lan, et al. Damage Quantification Assessment of Ultra-high Molecular Weight Polyethylene Based on Multiple Constraints[J]. Acta Armamentarii, 2026, 47(4): 250790.
DOI：

ZHANG Fubao, CHEN Hongyan, ZHOU Lan, et al. Damage Quantification Assessment of Ultra-high Molecular Weight Polyethylene Based on Multiple Constraints[J]. Acta Armamentarii, 2026, 47(4): 250790. DOI： 10.12382/bgxb.2025.0790.

摘要

超高分子量聚乙烯（Ultra-High Molecular Weight Polyethylene，UHMWPE）凭借其卓越的抗冲击性能与轻质高强特性，已成为单兵防护装备的核心材料，然而现有评估方法难以准确量化其复杂的损伤特征。针对UHMWPE材料在0°/90°铺层结构下的特殊损伤模式，提出了融合物理损伤特性约束的马尔可夫随机场K-means++模型。通过结合K-means++高效聚类、马尔可夫随机场空间约束和材料损伤特性建模，形成多重约束优化框架，引入的各向异性惩罚因子和叠加物理结构的距离度量有效捕捉了UHMWPE的十字与径向星形结构特征。实验结果表明：相较于传统分割方法与深度学习模型，所提算法在各损伤形态均展现出显著优势，特别是在各损伤形态的拉伸区和无损伤区域识别方面，误差降低幅度达90%以上；研究成果为UHMWPE软防护材料的防护性能评估与结构优化提供了定量化工具和可靠的技术支持。

Abstract

Ultra-high molecular weight polyethylene (UHMWPE) has emerged as the core material for individual soldier protection equipment due to its outstanding impact resistance

and its lightweight and high-strength characteristics. However

the existing evaluation methods are enable to accurately quantify the complex damage characteristics of UHMWPE. A Markov random field K-means++model with physical damage characteristic constraints is proposed for the special damage patterns of UHMWPE materials under a 0°/90° ply structure. This model forms a multi-constrained optimization framework by integrating the efficient clustering of K-means++

the spatial constraints of MRF

and the modeling of material damage characteristics. The cross and radial star structure features of UHMWPE are effectively captured by introducing the anisotropic penalty factor and the distance metric with superimposed physical structures. Experimental results indicate that the proposed model demonstrates significant advantages in all damage morphologies compared with traditional segmentation methods and deep learning models. Particularly in the identification of the tensile regions and undamaged areas of various damage morphologies

the error reduction rate exceeds 90% . This study provides a quantitative tool and reliable technical support for the evaluation of the protective performance and structural optimization of UHMWPE soft protective materials.

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