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Effect of martensite phase volume fraction on acoustic emission signals using wavelet packet analysis during tensile loading of dual phase steels
AbstractThis 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.
Effect of martensite phase volume fraction on acoustic emission signals using wavelet packet analysis during tensile loading of dual phase steels
AbstractThis 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.
Effect of martensite phase volume fraction on acoustic emission signals using wavelet packet analysis during tensile loading of dual phase steels
Khamedi, R. (author) / Fallahi, A. (author) / Refahi Oskouei, A. (author)
2010-01-13
8 pages
Article (Journal)
Electronic Resource
English
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