Femtosecond Laser Texturization on Coated Steel

Baldi-Boleda, Tomás; Colominas, Carles; García-Granada, A. A.

doi:10.3390/coatings12101602

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Ultrashort femtosecond lasers have recently been identified as a flexible and costeffective technology for texturing. Femtosecond laser sources can be integrated into CNC machines, allowing for the ablation of complex mold surfaces [30]. Moreover, controlling the laser texturing parameters allows for generating micro-and nano-scale features with the same equipment in a one-step process [31,32].…”

Section: Introductionmentioning

confidence: 99%

Wetting Characteristics of Laser-Ablated Hierarchical Textures Replicated by Micro Injection Molding

et al. 2023

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Texturing can be used to functionalize the surface of plastic parts and, in particular, to modify the interaction with fluids. Wetting functionalization can be used for microfluidics, medical devices, scaffolds, and more. In this research, hierarchical textures were generated on steel mold inserts using femtosecond laser ablation to transfer on plastic parts surface via injection molding. Different textures were designed to study the effects of various hierarchical geometries on the wetting behavior. The textures are designed to create wetting functionalization while avoiding high aspect ratio features, which are complex to replicate and difficult to manufacture at scale. Nano-scale ripples were generated over the micro-scale texture by creating laser-induced periodic surface structures. The textured molds were then replicated by micro-injection molding using polypropylene and poly(methyl methacrylate). The static wetting behavior was investigated on steel inserts and molded parts and compared to the theoretical values obtained from the Cassie–Baxter and Wenzel models. The experimental results showed correlations between texture design, injection molding replication, and wetting properties. The wetting behavior on the polypropylene parts followed the Cassie–Baxter model, while for PMMA, a composite wetting state of Cassie–Baxter and Wenzel was observed.

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Section: Introductionmentioning

confidence: 99%

Wetting Characteristics of Laser-Ablated Hierarchical Textures Replicated by Micro Injection Molding

et al. 2023

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Predictive ultrafast laser-induced formation of MoO_x using machine learning algorithms

Cano-Lara,

Espinal-Jimenez,

Camacho-López

et al. 2024

Appl. Opt.

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This research introduces an innovative methodology leveraging machine learning algorithms to predict the outcomes of experimental and numerical tests with femtosecond (fs) laser pulses on 500-nm-thick molybdenum films. The machine learning process encompasses several phases, including data acquisition, pre-processing, and prediction. This framework effectively simulates the interaction between fs laser pulses and the surface of molybdenum thin films, enabling precise control over the creation of MoO x phases. The exceptional precision of fs laser pulses in generating molybdenum oxides at localized micrometer scales is a significant advantage. In this study, we explored and evaluated 13 different machine learning methods for predicting oxide formation results. Our numerical results indicate that the extra trees (ET) and gradient boosting (GB) algorithms provide the best performance in terms of mean squared error, mean absolute error, and R-squared values: 48.44, 3.72, and 1.0 for ET and 32.25, 3.72, and 1.0 for GB. Conversely, support vector regression (SVR) and histogram gradient boosting (HGB) performed the worst, with SVR yielding values of 712.48, 15.27, and 0.163 and HGB yielding values of 434.29, 16.37, and 0.548. One of the most significant aspects of this research is that training these algorithms did not require hyperparameter optimization, and the training and validation process only needed 54 experimental samples. To validate this, we used a technique known as leave-one-out cross-validation, which is a robust validation method when the available data is limited. With this research, we aim to demonstrate the capability of machine learning algorithms in applications where data is limited due to the high cost of real experimentation, as is often the case in the field of optics.

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Femtosecond Laser Texturization on Coated Steel

Cited by 2 publications

References 26 publications

Wetting Characteristics of Laser-Ablated Hierarchical Textures Replicated by Micro Injection Molding

Wetting Characteristics of Laser-Ablated Hierarchical Textures Replicated by Micro Injection Molding

Predictive ultrafast laser-induced formation of MoO_x using machine learning algorithms

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Femtosecond Laser Texturization on Coated Steel

Cited by 2 publications

References 26 publications

Wetting Characteristics of Laser-Ablated Hierarchical Textures Replicated by Micro Injection Molding

Wetting Characteristics of Laser-Ablated Hierarchical Textures Replicated by Micro Injection Molding

Predictive ultrafast laser-induced formation of MoOx using machine learning algorithms

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Predictive ultrafast laser-induced formation of MoO_x using machine learning algorithms