Depth profiling of residual stress distribution in surface treated metallic structures using nonlinear ultrasonics

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Depth profiling of residual stress distribution in surface treated metallic structures using nonlinear ultrasonics
Title:
Depth profiling of residual stress distribution in surface treated metallic structures using nonlinear ultrasonics
Journal Title:
Ultrasonics
Publication Date:
15 October 2023
Citation:
Sampath, S., Liu, H., Tham, Z. W., Chen, Y. F., & Zhang, L. (2024). Depth profiling of residual stress distribution in surface treated metallic structures using nonlinear ultrasonics. Ultrasonics, 137, 107186. https://doi.org/10.1016/j.ultras.2023.107186
Abstract:
Robotic hammer peening (RHP) is a cold-working technique to improve the fatigue life of metallic structures by inducing compressive residual stress in the near-surface region. Measuring the depth profiling of residual stress distribution plays an important role in process design towards the advanced manufacturing of metallic structures. This study investigates the use of the nonlinear ultrasonic method to measure the residual stress profiles through the frequency-dependent penetration depth approach. Stainless steel specimens were treated with varying RHP intensities, and their morphological evolutions were characterized with a step-profilometer. The amplitudes of the second and third harmonic components of the longitudinal critically refracted (LCR) and Rayleigh waves were measured and analyzed. The effect of the surface roughness on the acoustic nonlinearity parameters before and after polishing at various peening intensities was briefly discussed. The results show a large variation in the acoustic nonlinearity parameter at the surface layer, indicating the potential of the proposed nonlinear ultrasonics for the measurement of depth profiling of residual stress.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the Singapore National Research Foundation - Central GAP Fund
Grant Reference no. : NRF2020NRF-CG001-035
Description:
ISSN:
0041-624X
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