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Laser texturing of AISI 304 stainless steel: experimental analysis and genetic algorithm optimisation to control the surface wettability

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Abstract

This paper deals with an experimental investigation of roughness influence on contact angle measurements and proposes a genetic algorithm to identify an empirical regression model to combine roughness and contact angles. A ns-pulsed laser was adopted to ablate different patterns on the surfaces of AISI 304 samples. During the tests, number of repetitions, hatch distance, laser scan speed and laser scanning strategy were changed. To assess the effect of these parameters on the wettability, a multilevel factorial design was developed and tested. The analysis of variance was adopted to determine which and how the laser parameters influence the roughness and the contact angle. A significant change in the wettability is due to the produced textures on the sample surfaces, with contact angles in the range 30–110°. The optimal regression model based on genetic algorithms was able to relate inputs and outputs with a mean error lower than 5%.

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Data availability

The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

Abbreviations

Adj MS:

Adjusted mean sum of squares

Adj SS:

Adjusted sum of squares

CB :

Cassie-Baxter state

C k :

Set of the chromosome

DF:

Total degrees of freedom

E max :

Pulse energy (mJ)

f :

Fraction of the wet solid surface

F :

Pulse frequency (kHz)

f f :

Fitness function

GA:

Genetic algorithm

H :

Height of the droplet (mm)

Hd :

Hatch distance (μm)

h(R, Ss, Hd):

Response variable

k j :

Coefficients of the GA regression model

l i :

Coefficients of the linear regression model

Lin:

Linear model

N :

Number of chromosomes

N C :

Number of combinations

N T :

Number of terms of the regression model

pHd, pR, pSs :

Powers of the input parameters

P N :

Nominal average power (W)

P P :

Peak power (kW)

r :

Radius of the droplet projected on the base (mm)

R :

Number of repetitions

Ra:

Average roughness (μm)

r f :

Roughness factor

rms:

Root mean square operation

RMS:

Root mean square error

S :

Scanning strategy

Ss :

Laser scanning speed (mm/s)

Sdr:

Developed surface area ratio

Std:

Standard deviation

t D :

Pulse duration [ns]

W :

Wenzel state

wt%:

Weight percentage

y(R, Ss, Hd):

Measured value

θ CB :

Cassie-Baxter’s contact angle (°)

θ W :

Wenzel’s contact angle (°)

θ Y :

Young’s contact angle (°)

ϑ :

Contact angle (°)

λ :

Laser wavelength (nm)

Π:

Contribution percentage

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Authors and Affiliations

  1. Department of Enterprise Engineering, University of Rome ‘Tor Vergata’, Via del Politecnico 1, 00133, Rome, Italy

    Silvio Genna

  2. CIRTIBS Research Centre, University of Naples Federico II, P.le Tecchio 80, 80125, Naples, Italy

    Silvio Genna

  3. University of Rome ‘Niccolò Cusano’, Via Don Carlo Gnocchi 3, 00166, Rome, Italy

    Oliviero Giannini, Stefano Guarino, Gennaro Salvatore Ponticelli & Flaviana Tagliaferri

  4. University of Applied Sciences Mittweida, Technikumplatz 17, D-09648, Mittweida, Germany

    Flaviana Tagliaferri

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  1. Silvio Genna
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  2. Oliviero Giannini
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  3. Stefano Guarino
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  4. Gennaro Salvatore Ponticelli
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  5. Flaviana Tagliaferri
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Contributions

Conceptualisation, Silvio Genna and Stefano Guarino; methodology, Silvio Genna and Flaviana Tagliaferri; software, Oliviero Giannini and Gennaro Salvatore Ponticelli; validation, Silvio Genna, Flaviana Tagliaferri and Gennaro Salvatore Ponticelli; formal analysis, Oliviero Giannini, Stefano Guarino, Gennaro Salvatore Ponticelli and Flaviana Tagliaferri; investigation, Silvio Genna and Flaviana Tagliaferri; writing (original draft), Gennaro Salvatore Ponticelli and Flaviana Tagliaferri; writing (review and editing), Silvio Genna, Oliviero Giannini, Stefano Guarino and Gennaro Salvatore Ponticelli.

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Correspondence to Gennaro Salvatore Ponticelli.

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Genna, S., Giannini, O., Guarino, S. et al. Laser texturing of AISI 304 stainless steel: experimental analysis and genetic algorithm optimisation to control the surface wettability. Int J Adv Manuf Technol 110, 3005–3022 (2020). https://doi.org/10.1007/s00170-020-06073-4

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