Laser texturing of AISI 304 stainless steel: experimental analysis and genetic algorithm optimisation to control the sur
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ORIGINAL ARTICLE
Laser texturing of AISI 304 stainless steel: experimental analysis and genetic algorithm optimisation to control the surface wettability Silvio Genna 1,2 & Oliviero Giannini 3 & Stefano Guarino 3 & Gennaro Salvatore Ponticelli 3
&
Flaviana Tagliaferri 3,4
Received: 25 May 2020 / Accepted: 9 September 2020 # Springer-Verlag London Ltd., part of Springer Nature 2020
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%. Keywords Roughness . Wettability . Fibre laser . Laser texturing . Genetic algorithm
Abbreviations Adj MS Adj SS CB Ck DF Emax f F ff GA H
Adjusted mean sum of squares Adjusted sum of squares Cassie-Baxter state Set of the chromosome Total degrees of freedom Pulse energy (mJ) Fraction of the wet solid surface Pulse frequency (kHz) Fitness function Genetic algorithm Height of the droplet (mm)
* Gennaro Salvatore Ponticelli [email protected] 1
Department of Enterprise Engineering, University of Rome ‘Tor Vergata’, Via del Politecnico 1, 00133 Rome, Italy
2
CIRTIBS Research Centre, University of Naples Federico II, P.le Tecchio 80, 80125 Naples, Italy
3
University of Rome ‘Niccolò Cusano’, Via Don Carlo Gnocchi 3, 00166 Rome, Italy
4
University of Applied Sciences Mittweida, Technikumplatz 17, D-09648 Mittweida, Germany
Hd h(R, Ss, Hd) kj li Lin N NC NT pHd, pR, pSs PN PP r R Ra rf rms RMS S Ss Sdr Std tD W wt%
Hatch distance (μm) Response variable Coefficients of the GA regression model Coefficients of the linear regression model Linear model Number of chromosomes Number of combinations Number of terms of the regression model Powers of the input parameters Nominal average power (W) Peak power (kW) Radius of the droplet projected on the base (mm) Number of repetitions Average roughness (μm) Roughness factor Root mean square operation Root mean square error Scanning strategy Laser scanning speed (mm/s) Developed surface area ratio Standard deviation Pulse duration [ns] Wenzel state Weight percentage
Int J Adv Manuf Technol
y(R, Ss, Hd) θCB θW θY ϑ λ Π
Measured value Cassie-Baxter’s contact angle (°) Wenzel’s contact angle (°) Young’s contact angle (°) Contact angle (°)
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