journal article·2026-07Open access

Laser cleaning behavior and mechanism analysis of surface contaminants on nickel-based superalloy

Ming Liu, Wei Guo, Yihui Gao, Haolin Zeng, Zhengming Liu, Zhu Li, Jianhua Yao

Abstract

: Surface contaminants on the GH3600 nickel-based superalloy—including organic residues, and native oxides—are difficult to remove using conventional methods without damaging the substrate. This study systematically evaluates the cleaning performance of a MOPA-type nanosecond pulsed laser and its subsequent impact on substrate integrity. The influence of laser fluence on contaminant removal and substrate response was assessed using a suite of complementary characterization techniques (SEM, LSCM, EDS, FTIR, XPS, TEM, XRD and mechanical testing). An optimal cleaning fluence of 6.11 J/cm 2 was established, resulting in a relative carbon reduction of 34.7% (from 31.18 to 20.55 at.%) and an oxygen reduction of 61.6% (from 7.03 to 2.70 at.%). Under these conditions characteristic organic functional peaks in the FTIR spectra disappeared, and the water contact angle transitioned from 69.17° to 18.96°. Furthermore, the contaminants were effectively removed while the substrate exhibited no detectable remelting or mechanical degradation; notably, the fatigue life improved by approximately 28.4%. Mechanistic analysis indicates an energy-dependent sequence: at low fluence (3.06 J/cm 2 ) removal is dominated by photothermal vaporization and brittle delamination; at moderate fluence (6.11 J/cm 2 ) synergistic evaporation, molten ejection and interfacial delamination prevail; at high fluence (9.17 J/cm 2 ) intense plasma formation and substrate remelting cause secondary oxidation. These findings provide experimental guidance and mechanistic insights for the high-precision laser cleaning of aero-engine components.
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Research context

OpenAlex topics: Laser Material Processing Techniques · Laser-Ablation Synthesis of Nanoparticles · Additive Manufacturing Materials and Processes

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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