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Peening

About: Peening is a research topic. Over the lifetime, 5538 publications have been published within this topic receiving 73073 citations.


Papers
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Patent
26 Feb 1968

32 citations

Journal ArticleDOI
TL;DR: In this paper, the influence of shot peening treatment on reversible and irreversible hydrogen embrittlement of PSB1080 steel was investigated using slow strain rate tensile tests and electrochemical hydrogen charging-induced cracking tests, respectively.

32 citations

Book ChapterDOI
TL;DR: In this paper, the authors investigated multiaxial fatigue of shot peened Al-7075-T651 alloy with plain axi-symmetric specimens under in-phase tension and torsion loading, under nominal load ratio R = 0.05 and bao-iality ratio λ = τ a /σ a = 2.

31 citations

Journal ArticleDOI
TL;DR: In this paper, the residual stresses induced by LSP in a medical-grade Ti-6Al-7Nb alloy were characterized by using Incremental Hole Drilling (IHD) with predictive modelling using an Artificial Neural Network (ANN).
Abstract: Prolonged usage can lead to fatigue failures in prosthetic implants. This is an emerging concern for the prosthesis designer and as well as for end-users. Laser shock peening (LSP) can be used to improve the life of prostheses by introducing a compressive surface stress that delays or arrests the initiation of cracks. We report measurements of residual stresses induced by LSP in a medical-grade Ti-6Al-7Nb alloy. In this paper, the LSP introduced residual stress field was characterized by using Incremental Hole Drilling (IHD) with predictive modelling using an Artificial Neural Network (ANN). The Ti-6Al-7Nb samples were processed by a pulsed Nd: YAG laser with laser energy of 3 J, 5 J and 7 J with overlaps of 33%, 50% and 67%. The IHD results show that a range of −42 MPa to −516 MPa compressive residual stresses are formed at the near surface. The residual stress value and affecting layer depth are proportional to laser energy and overlap. What is more, a gradient descent learning algorithm was employed in ANN prediction. The microhardness, laser energy, overlap and depth are set as input parameters while the residual stresses are output. The predicted results have a good agreement with the experimental data with accuracy of 96.16% and 95.16%. It proves that ANN is applicable in LSP residual stress simulation when the experimental data is limited. The work in this paper enables to have subsequent predictions of fatigue life of implants via residual stress fields for future applications.

31 citations

Journal ArticleDOI
TL;DR: In this article, a photonic Doppler velocimetry system was used to track the velocity of the back free surface induced by laser generated shock pressure of this regime.
Abstract: The shock pressure generated by the glass confined regime in laser shock peening and its attenuation in the target material are investigated. First, the particle velocity of the target back free surface induced by laser generated shock pressure of this regime is measured using a photonic Doppler velocimetry system. The temporal profile of the particle velocity at the back free surface, where the elastic precursor is captured, manifests a powerful diagnostic capability of this newly developed photonic Doppler velocimetry system for tracking the velocity on short time scales in shock-wave experiments. Second, a coupling pressure analytical model, in which the material constitutive models of confined layers and target material are considered, is proposed to predict the plasma pressure profile at the surface of target. Furthermore, using the predicted shock pressure profile as the input condition, the dynamic response of the target under the shock pressure is simulated by LS-DYNA. The simulated back free surface velocity profile agrees well with that measured by the photonic Doppler velocimetry system. Finally, the attenuation behavior of stress waves and particle velocities in the depth of the target is analyzed, and it indicates an exponential decay. The corresponding empirical formulas for the attenuation behavior are given based on the numerical results.

31 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023256
2022500
2021282
2020303
2019340
2018305