Laser texturing is increasingly gaining attention in the field of metal alloys due to its ability to improve surface
properties, particularly in steel alloys. However, the input parameters of the technology must be carefully
controlled to achieve the desired surface roughness. Roughness is critical to the activation of the surface before
further bonding operations, and it is often assessed using several parameters such as Ra, Rt, Rz, and Rv. This
surface activation affects the properties of the metal alloy in terms of wettability, which has been evaluated by
the deposition of ethylene glycol droplets through a contact angle. This allowed a direct relationship to be
established 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 future
applications in improving the behaviour of metallic alloys. In this research, a comparative analysis between
Response Surface Models (RSM) and predictive models based on Artificial Neural Networks (ANN) has been
conducted. The model based on neural networks was able to predict all the output variables with a fit greater
than 90%., improving that obtained by RSM. The model obtained by ANN allows a greater adaptability to the
variation of results obtained, reaching deviations close to 0.2 μm. The influence of input parameters, in particular
power and scanning speed, on the achieved roughness and surface wettability has been figured out by contact
angle measurements. This increases its surface activation in terms of wettability. Superhydrophilic surfaces were
achieved by setting the power to 20 W and scanning speed to ten mm/s. In contrast, a power of 5 W and a
scanning speed of 100 mm/s reduced the roughness values.
