...
首页> 外文期刊>Materials & design >Effect of martensite phase volume fraction on acoustic emission signals using wavelet packet analysis during tensile loading of dual phase steels
【24h】

Effect of martensite phase volume fraction on acoustic emission signals using wavelet packet analysis during tensile loading of dual phase steels

机译:小波包分析在双相钢拉伸加载过程中马氏体相体积分数对声发射信号的影响

获取原文
获取原文并翻译 | 示例
           

摘要

This paper will investigate the application of wavelet-based Acoustic Emission (AE) signal processing on micromechanisms to identify failure in dual phase steels (DPS)s. The AE signals from a tensile test using a range of DPS with different volume fractions of martensite (VM)s, in the range of 11-65% VM, were obtained and their waveforms were decomposed into various wavelet levels, each of which was related to a specific frequency range. Each level includes precise details, or approximations, of the so-called components. The energy percentage of each component was obtained by comparing it with the total energy of the AE signal. The energy distribution criterion in each component indicates that the energy in the AE signals is essentially concentrated on two or three components within a distinct frequency range. Each frequency range is related to a separate micromechanism, identifying failure. The results found for low VM in the contribution of ferrite/martensite interface decohesion figure prominently because 48% of their total energy was related to this micromechanism for a sample with 11 % VM. The contribution of martensite phase fracture increased from 12% to 48.3% of total energy with an increase of VM in the range of 11% to 65% VM. The results were verified with microscopic observations and they indicate that wavelet-based signal processing is an efficient tool in the analysis of AE signals to detect micromechanisms identifying failure in DPS.
机译:本文将研究基于小波的声发射(AE)信号处理在微机制上的应用,以识别双相钢(DPS)的故障。使用一系列DPS进行拉伸测试,得到的AE信号具有不同的马氏体(VM)体积分数(VM在11-65%的范围内),并将其波形分解为各种小波电平,每个小波电平都与到特定的频率范围。每个级别都包含所谓组件的精确细节或近似值。通过将其与AE信号的总能量进行比较,可以得出每种组分的能量百分比。每个分量中的能量分配标准表明,AE信号中的能量基本上集中在不同频率范围内的两个或三个分量上。每个频率范围都与单独的微机械有关,以识别故障。对于低VM的结果,铁素体/马氏体界面脱粘的贡献显着,因为对于VM含量为11%的样品,其总能量的48%与这种微观机制有关。马氏体相断裂的贡献从总能量的12%增加到48.3%,VM的增加幅度在11%到65%的范围内。显微镜观察结果验证了结果,它们表明基于小波的信号处理是分析AE信号以检测识别DPS失败的微观机制的有效工具。

著录项

  • 来源
    《Materials & design》 |2010年第6期|2752-2759|共8页
  • 作者单位

    Mechanical Engineering Department, Amirkabir University of Technology, 424 Hafez Ave., Tehran 15916-34311, Iran;

    rnMechanical Engineering Department, Amirkabir University of Technology, 424 Hafez Ave., Tehran 15916-34311, Iran;

    rnMechanical Engineering Department, Amirkabir University of Technology, 424 Hafez Ave., Tehran 15916-34311, Iran;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    acoustic emission; dual phase steel; micromechanisms identifying failure;

    机译:声发射双相钢识别故障的微观机制;

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