RT Journal Article
T1 Predictive models based on RSM and ANN for roughness and wettability achieved by laser texturing of S275 carbon steel alloy
A1 Bañón-García, Fermín
A1 Martín-Béjar, Sergio
A1 Vázquez-Martínez, J. M.
A1 Salguero, J.
A1 Trujillo-Vilches, Francisco Javier
K1 Ciencia de los materiales
K1 Acero al carbono
AB Laser texturing is increasingly gaining attention in the field of metal alloys due to its ability to improve surfaceproperties, particularly in steel alloys. However, the input parameters of the technology must be carefullycontrolled to achieve the desired surface roughness. Roughness is critical to the activation of the surface beforefurther bonding operations, and it is often assessed using several parameters such as Ra, Rt, Rz, and Rv. Thissurface activation affects the properties of the metal alloy in terms of wettability, which has been evaluated bythe deposition of ethylene glycol droplets through a contact angle. This allowed a direct relationship to beestablished between the final roughness, the wettability of the surface and the texturing parameters of the alloy.This raises the interest of being able to predict the behaviour in terms of roughness and wettability for futureapplications in improving the behaviour of metallic alloys. In this research, a comparative analysis betweenResponse Surface Models (RSM) and predictive models based on Artificial Neural Networks (ANN) has beenconducted. The model based on neural networks was able to predict all the output variables with a fit greaterthan 90%., improving that obtained by RSM. The model obtained by ANN allows a greater adaptability to thevariation of results obtained, reaching deviations close to 0.2 μm. The influence of input parameters, in particularpower and scanning speed, on the achieved roughness and surface wettability has been figured out by contactangle measurements. This increases its surface activation in terms of wettability. Superhydrophilic surfaces wereachieved by setting the power to 20 W and scanning speed to ten mm/s. In contrast, a power of 5 W and ascanning speed of 100 mm/s reduced the roughness values.
PB Elsevier
YR 2023
FD 2023-08-22
LK https://hdl.handle.net/10630/27769
UL https://hdl.handle.net/10630/27769
LA eng
NO F. Bañon, S. Martin, J.M. Vazquez-Martinez, J. Salguero, F.J. Trujillo, Predictive models based on RSM and ANN for roughness and wettability achieved by laser texturing of S275 carbon steel alloy, Optics & Laser Technology, Volume 168, 2024, 109963, ISSN 0030-3992, https://doi.org/10.1016/j.optlastec.2023.109963
NO Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 24 ene 2026