中国塑料 ›› 2008, Vol. 22 ›› Issue (07): 92-96 .DOI: 10.19491/j.issn.1001-9278.2008.07.019

• 标准与测试 • 上一篇    下一篇

PE-UHMW /PE-LD层合板拉伸破坏声发射信号参数相关性分析

王旭,张慧萍,晏雄   

  1. 教育部面料技术重点实验室,东华大学纺织学院,上海201620
  • 收稿日期:2008-04-12 修回日期:1900-01-01 出版日期:2008-07-26 发布日期:2008-07-26

Correlation Between Acoustic Emission Parameters of PE-UHMW/PE-LDLaminates during Tensile Damage Process

WANG Xu,ZHANG Hui一ping,YAN Xiong   

  1. Key Lab of Textile Science and Technology, Ministry of Education, College of Textiles,Donghua University, Shanghai 201620, China
  • Received:2008-04-12 Revised:1900-01-01 Online:2008-07-26 Published:2008-07-26
  • Contact: WANG Xu

摘要: 研究了超高相对分子质量聚乙烯抵密度聚乙烯( PE-UHMW /PE-LD)单向层合板拉伸破坏过程中的声发射信号参数之间的相关性,并进行了主成分分析。结果表明,声发射参数间存在较强的相关性,其中[0]层合板(加载方向与纤维方向平行)持续时间和振铃计数之间相关系数达到0.976,[90〕层合板(加载方向与纤维方向垂直)振幅和振铃计数之间相关系数达到0.8。主成分分析显示[0]和[90]层合板第一主成分主要反映上升时间、持续时间等信号的时域特点,第二主成分主要反映峰值频率、频率重心等信号的频域特点。通过[90〕层合板声发射信号的主成分得分图,显示出界面裂纹产生、扩展和界面分离等不同损伤模式对应不同的主成分得分。

关键词: 复合材料, 损伤, 声发射, 参数分析, 相关性分析, 主成分分析

Abstract: The purpose of this paper is to investigate acoustic emission (AE)characteristic of ultra high molecular weight polycthylene/low density polyethylene (PE-UHMW/PE-LD) laminates during tensile damage process by means of correlation analysis and principal componentanalysis. The result of experi-
mcnt reveals strong correlation between AE parameters. For example, the correlation coefficient betwecn duration time and counts of [0] is 0.976,while that between amplitude and counts of[90] is 0 .8.According to principal component analysis, the first principal components of [0] and[90] reflect
the character of time domain,such as rise time and duration time, and the second principal components of them reflect the character of frequency domain,such as peak frequency and frequency centriod. The principal component score of[90] shows different damage modes, such as the onset of interface crack- ing, the growth of interface cracking and the fracture of interface, each possessing different scores.

Key words: composite, damage, acoustic emission, parameter analysis, correlation analysis, principalcomponent analysis