Acoustic Emission (AE) technique is used for characterizing the corrosion behaviour of the AlSi10Mg specimens prepared from recycled feed-material using the Selective Laser Melting (SLM) Process. The corrosion behaviour is studied by exposing the specimens to a simulated salt attack for a period of 240 h. AE signals are recorded for the entire duration and are analysed in their time-frequency domain. Initially, noises are observed in the recorded AE signals. A methodology based on waveform entropy is developed to denoise the signals. The characteristics of the noise are studied to retain any useful information. The results show that some of the low amplitude signals could be misidentified as noise signals; it is rectified by studying the features of the noise such as peak frequency, frequency centroid and peak amplitude. The time-frequency characteristics of the AE signals are then studied along with the morphological features of the specimens exposed to different stages of corrosion. This provides a correlation between the corrosion behaviour such as pit formation, corrosion product formation and the corrosion crack formation and the time-frequency characteristics of AE signals. This information can identify the corrosion behaviour of the AlSi10Mg specimens intuitively.
Acoustic emission signal processing for the assessment of corrosion behaviour in additively manufactured AlSi10Mg
Renna, G.
2022-01-01
Abstract
Acoustic Emission (AE) technique is used for characterizing the corrosion behaviour of the AlSi10Mg specimens prepared from recycled feed-material using the Selective Laser Melting (SLM) Process. The corrosion behaviour is studied by exposing the specimens to a simulated salt attack for a period of 240 h. AE signals are recorded for the entire duration and are analysed in their time-frequency domain. Initially, noises are observed in the recorded AE signals. A methodology based on waveform entropy is developed to denoise the signals. The characteristics of the noise are studied to retain any useful information. The results show that some of the low amplitude signals could be misidentified as noise signals; it is rectified by studying the features of the noise such as peak frequency, frequency centroid and peak amplitude. The time-frequency characteristics of the AE signals are then studied along with the morphological features of the specimens exposed to different stages of corrosion. This provides a correlation between the corrosion behaviour such as pit formation, corrosion product formation and the corrosion crack formation and the time-frequency characteristics of AE signals. This information can identify the corrosion behaviour of the AlSi10Mg specimens intuitively.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
