A Review of Effects of Femtosecond Laser Parameters on Metal Surface Properties

Sun, Haitao; Li, Jiuxiao; Liu, Mingliang; Yang, Dongzi; Li, Fangjie

doi:10.3390/coatings12101596

Cited by 23 publications

(10 citation statements)

References 111 publications

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“…In the following sections, two typical functionalization approaches are briefly addressed (cf. [ 141 ].…”

Section: Surface Functionalizationmentioning

confidence: 99%

Dynamics and Processes on Laser-Irradiated Surfaces

Reif

2023

Nanomaterials

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The modification of solid surfaces via the impacts of intense laser pulses and the dynamics of the relevant processes are reviewed. We start with rather weak interactions on dielectric materials, based on non-linear absorption across the bandgap and resulting in low-level local effects like electron and individual ion emission. The role of such locally induced defects in the cumulative effect of incubation, i.e., the increase in efficiency with the increasing number of laser pulses, is addressed. At higher excitation density levels, due to easier laser–material coupling and higher laser fluence, the energy dissipation is considerable, leading to lattice destabilization, surface relaxation, ablation, and surface modification (e.g., laser-induced periodic surface structures). Finally, a short list of possible applications, namely in the field of wettability, is presented.

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“…In the following sections, two typical functionalization approaches are briefly addressed (cf. [ 141 ].…”

Section: Surface Functionalizationmentioning

confidence: 99%

Dynamics and Processes on Laser-Irradiated Surfaces

Reif

2023

Nanomaterials

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“…[43][44][45][46] Contemporary theories regarding the genesis of aligned LIPSS can be classified into two primary categories: electromagnetic models and self-organizational models. [47][48][49][50] This dichotomy, however, is not exclusive, as it is now widely acknowledged that the observed structures are outcomes of synergistic electromagnetic and hydrodynamic influences. [51][52][53][54] Models postulating the existence of electromagnetic modes delineate the energy distribution across the surface, incorporating resonances such as surface plasmon polariton, cylindrical surface waves, and surface lattice resonance.…”

Section: Introductionmentioning

confidence: 99%

Beyond the Microscale: Advances in Surface Nanopatterning by Laser‐Driven Self‐Organization

Nakhoul,

Colombier

2024

Laser & Photonics Reviews

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Designing complex local properties that seamlessly integrate efficient functions into processed materials presents a formidable challenge. A promising solution has emerged in the form of ultrafast laser‐surface structuring. Through time‐controlled polarization ultrafast irradiation at the picosecond timescale, spontaneous self‐organization of surfaces can be induced. The thermal gradient length scale unfolds on the micro‐ and nanoscale, instigating thermoconvection that leads to structured surfaces upon quenching. Convective instabilities dynamically shape intricate yet self‐regulated periodic relief structures. The ability to achieve laser‐induced self‐organization in both surface dimensions holds immense scientific importance, as it unlocks the potential to create uniform periodic 2D patterns by harnessing the inherent regulation of nonlinear dynamics processes in fluids. This comprehensive review explores recent advances in understanding and leveraging ultrafast laser‐induced self‐organization for precise patterning across versatile scales and applications. The insights herein hold the potential to drive significant advancements in nanoscale manufacturing through 2D laser‐induced periodic surface structures.

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“…Laser ablation is a non-contact processing method that uses a focused spot of high energy density to instantly vaporize surface materials and is becoming an effective means of processing surface microstructures on hard-to-machine material [ 3 , 4 ]. The processing quality of laser ablation depends on the selection of process parameters, such as laser power, pulse frequency, and scanning speed [ 5 ]. Based on theoretical analysis and experimental results, choosing the right combination of process parameters can significantly improve the quality of laser ablation [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Surface Feature Prediction for Laser Ablated 40Cr13 Stainless Steel Based on Extreme Learning Machine

et al. 2023

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Determining an optimal combination of laser process parameters can significantly improve the efficiency and quality of 40Cr13 steel surface processing. In this study, two machine learning models (ELMSS and ELMPS) were proposed to predict the processing results of surface features to optimize process parameters. The prediction accuracies of the proposed models were always higher than those of traditional back propagation (BP) and radial basis function (RBF) neural networks, and the calculation time of the proposed models was significantly reduced. In comparison, the prediction accuracy ranking for ablation depth was ELMSS (92.6%), BP (89.8%), and RBF (89.6%), and for the ablation width, it was ELMSS (98.3%), BP (97.4%), and RBF (96.1%). The material removal rate was 92.4%, 91.1%, and 89.1% for ELMSS, BP, and RBF,respectively. Finally, the prediction accuracy ranking for surface roughness was 86.8%, 80.7%, and 79.5% for ELMPS, BP, and RBF, respectively. After optimization by the genetic algorithm, the prediction accuracies of the proposed models for the depth, width, material removal rate, and surface roughness reached 94.0%, 99.0%, 93.2%, and 91.2%, respectively. With the support of ELMSS and ELMPS, the results of the surface features can be predicted before machining and the appropriate process parameters can be selected in advance.

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A Review of Effects of Femtosecond Laser Parameters on Metal Surface Properties

Cited by 23 publications

References 111 publications

Dynamics and Processes on Laser-Irradiated Surfaces

Dynamics and Processes on Laser-Irradiated Surfaces

Beyond the Microscale: Advances in Surface Nanopatterning by Laser‐Driven Self‐Organization

Surface Feature Prediction for Laser Ablated 40Cr13 Stainless Steel Based on Extreme Learning Machine

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