Effect of Laser Shock Peening on Microstructure and Micro-texture Evolution in High-Strength Low-Alloy Steel upon Electrochemical Interaction
Source abstract
Abstract
In this work, the effect of laser shock peening (LSP) on the electrochemical behavior of HSLA steel (ASTM A 588 Grade D) in 3.5 wt.% NaCl solution was investigated. The microstructural evolution, micro-texture development, surface roughness, residual stress development, microhardness, dislocation density, potentiodynamic polarization, and electrochemical impedance spectroscopy were studied in depth. The corroded surfaces (with and without LSP) were characterized using scanning electron microscopy and Raman spectroscopy for understanding the nature of the passive film formation and its stability. The results revealed that LSP treatment marginally refines the near-surface microstructure from 9.1 to 7.4 μm and generated high compressive residual stress (CRS) of about 500 MPa on the top surface; such variation happened due to severe plastic deformation during LSP. The micro-texture analyses of the laser-peened specimens (on the surface) revealed that {011} <111> texture components were significantly increased while the γ-fiber weakened after laser-peening. Dislocation density was obtained ~ 9.266 × 1012 on the unpeened and ~ 1.158 × 1013 on the laser-peened surfaces, indicating that higher pressure shock waves plastically deformed the top layer of the peened specimen. The corrosion studies indicated about 4 times improvement in the corrosion resistance of the peened specimens—the combined effects of LSP-induced gradient CRS and refined microstructure improved the corrosion resistance. The EDS and Raman spectroscopy results demonstrated the persistence of a compact and continuous passive oxide film/layer, primarily made of Fe2O3, on the surface of the laser-peened specimens.
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Research context
OpenAlex topics: Surface Treatment and Residual Stress · Erosion and Abrasive Machining · High-Temperature Coating Behaviors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (65)
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