首页> 外文期刊>Polymer Composites >Classification and Identification of Damage Mechanisms in Polyethylene Self-Reinforced Laminates by Acoustic Emission Technique
【24h】

Classification and Identification of Damage Mechanisms in Polyethylene Self-Reinforced Laminates by Acoustic Emission Technique

机译:声发射技术对聚乙烯自增强层合板损伤机理的分类识别

获取原文
获取原文并翻译 | 示例
       

摘要

The objective of present study is to classify and identify damage mechanisms in polyethylene(PE) self-reinforced composites by acoustic emission (AE) technique. Model specimens including LDPE resin, [90°]laminate, single fiber composite, fiber bundle composite, and [±45°] laminates are fabricated to obtain expected damage mechanisms during tensile testing. First, mechanical behaviors and corresponding AE response of model specimens are studied to validate damage mechanisms in UHMWPE/LDPE laminates. Second, relationship among AE descriptors is investigated by hierarchical cluster analysis, and AE signals are classified by k-means cluster analysis. Correlations between damage mechanisms and AE are established in terms of amplitude, duration, and peak frequency of AE signals. Finally, an artificial neural network is created and trained by various optimal algorithms to identify damage mechanisms. The results reveal that typical damage mechanisms in PE self-reinforced composite can be classified in terms of the similarity between AE signals and identified by trained artificial neural network.
机译:本研究的目的是通过声发射(AE)技术对聚乙烯(PE)自增强复合材料的损伤机理进行分类和识别。制作了包括LDPE树脂,[90°]层压板,单纤维复合材料,纤维束复合材料和[±45°]层压板的模型样品,以在拉伸试验中获得预期的破坏机理。首先,研究了模型样品的力学行为和相应的AE响应,以验证UHMWPE / LDPE层压板的损伤机理。其次,通过层次聚类分析研究AE描述符之间的关系,并通过k均值聚类分析对AE信号进行分类。损害机制与AE之间的相关性是根据AE信号的幅度,持续时间和峰值频率确定的。最后,通过各种最佳算法创建并训练了一个人工神经网络,以识别损伤机理。结果表明,PE自增强复合材料的典型损伤机理可以根据AE信号之间的相似性进行分类,并通过训练的人工神经网络进行识别。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号