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HomeKey Engineering MaterialsKey Engineering Materials Vol. 1050Integrated Artificial Neural Network-Based...

Integrated Artificial Neural Network-Based Approach for Predicting Surface Roughness Parameters in Laser Surface Texturing of Ti6Al4V

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Abstract:

Laser surface texturing (LST) is an effective technique for tailoring the surface properties of Ti6Al4V alloy, widely employed in biomedical applications where surface topography plays a key role in osseointegration and functional performance. Nevertheless, the strong nonlinear relationship between laser process parameters and resulting surface roughness still limits predictive control of laser-textured surfaces. This work presents an experimental study aimed at investigating the influence of laser surface texturing parameters on the surface morphology of Ti6Al4V. Key process variables, including laser power, scanning speed, pulse frequency, pulse duration and overlap percentage are systematically varied using a fiber laser system. The textured surfaces are characterized through three-dimensional surface roughness parameters, namely Sa, Sz, Sku, Svk, and Ssk, providing a detailed quantitative description of surface topography relevant for biomedical applications. The resulting experimental dataset represents a fundamental basis for the subsequent development of artificial intelligence models, based on neural networks, for predicting surface roughness parameters as a function of laser processing conditions. The proposed approach supports data-driven optimization of laser surface texturing processes within intelligent and sustainable manufacturing frameworks.

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Advanced Numerical and AI Strategies for Material Forming

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Periodical:

Key Engineering Materials (Volume 1050)

Pages:

249-257

DOI:

https://doi.org/10.4028/p-F27Mqr

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Cite this paper

Online since:

April 2026

Authors:

Agata Sposato, Giovanni Carpino, Maria Rosaria Saffioti*, Giovanna Rotella, Domenico Umbrello

Keywords:

Artificial Neural Network, Laser Surface Treatment, Surface Roughness

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Creative Commons CC BY 4.0

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* - Corresponding Author

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