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<p>This study determined acoustic emission AE-based wavelet energy to characterize fatigue damage behavior under block spectrum loading. Acoustic emission signals were insufficient for interpreting fatigue damage behavior. Hence, further strategies were vital to enhance the characterization of failure. AE signals were continuously monitored and analyzed using continuous wavelet transform (CWT) to decompose them into time-frequency components, thereby facilitating the detection of damage phenomena. Analysis of wavelet coefficients and energy demonstrated a marked increase in AE activity during the crack propagation phase. The derived AE signals from CWT and power spectral density (PSD) for the cumulative energy decomposition revealed strong correlations with fatigue cycles, as demonstrated by high R² values exceeding 0.95. Both signals also showed high accuracy as all the data values lay within the 95% confidence interval. This approach offers significant implications for improving the durability and safety of AlSi10Mg components in applications subjected to various loading conditions.</p>

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On the need to characterize fatigue damage behavior in additively manufactured AlSi10Mg using signal energy parameters

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • M. M. Mubasyir,
  • S. Abdullah,
  • M. K. Faidzi,
  • S. S. K. Singh,
  • C. H. Chin,
  • Z. Wahid

摘要

This study determined acoustic emission AE-based wavelet energy to characterize fatigue damage behavior under block spectrum loading. Acoustic emission signals were insufficient for interpreting fatigue damage behavior. Hence, further strategies were vital to enhance the characterization of failure. AE signals were continuously monitored and analyzed using continuous wavelet transform (CWT) to decompose them into time-frequency components, thereby facilitating the detection of damage phenomena. Analysis of wavelet coefficients and energy demonstrated a marked increase in AE activity during the crack propagation phase. The derived AE signals from CWT and power spectral density (PSD) for the cumulative energy decomposition revealed strong correlations with fatigue cycles, as demonstrated by high R² values exceeding 0.95. Both signals also showed high accuracy as all the data values lay within the 95% confidence interval. This approach offers significant implications for improving the durability and safety of AlSi10Mg components in applications subjected to various loading conditions.