Machine learning model for predicting surface wettability in laser-textured metal alloys

Mohammad Mohammadzadeh Sanandaji; Danial Ebrahimzadeh; Mohammad Ikram Haider; Yaser Mike Banad; Aleksandar Poleksic; Hongtao Ding

doi:10.48550/arxiv.2601.11661

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Machine learning model for predicting surface wettability in laser-textured metal alloys

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Machine learning model for predicting surface wettability in laser-textured metal alloys

Mohammad Mohammadzadeh Sanandaji, Danial Ebrahimzadeh, Mohammad Ikram Haider, Yaser Mike Banad, Aleksandar Poleksic and Hongtao Ding

ArXiv.org

Cornell University

01/15/2026

DOI: 10.48550/arxiv.2601.11661

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Surface wettability, governed by both topography and chemistry, plays a critical role in applications such as heat transfer, lubrication, microfluidics, and surface coatings. In this study, we present a machine learning (ML) framework capable of accurately predicting the wettability of laser-textured metal alloys using experimentally derived morphological and chemical features. Superhydrophilic and superhydrophobic surfaces were fabricated on AA6061 and AISI 4130 alloys via nanosecond laser texturing followed by chemical immersion treatments. Surface morphology was quantified using the Laws texture energy method and profilometry, while surface chemistry was characterized through X-ray photoelectron spectroscopy (XPS), extracting features such as functional group polarity, molecular volume, and peak area fraction. These features were used to train an ensemble neural network model incorporating residual connections, batch normalization, and dropout regularization. The model achieved high predictive accuracy (R2 = 0.942, RMSE = 13.896), outperforming previous approaches. Feature importance analysis revealed that surface chemistry had the strongest influence on contact angle prediction, with topographical features also contributing significantly. This work demonstrates the potential of artificial intelligence to model and predict wetting behavior by capturing the complex interplay of surface characteristics, offering a data-driven pathway for designing tailored functional surfaces.

Computer Science - Learning

Physics - Materials Science

Details

Title: Subtitle: Machine learning model for predicting surface wettability in laser-textured metal alloys
Creators: Mohammad Mohammadzadeh Sanandaji - University of Iowa
Danial Ebrahimzadeh - University of Oklahoma
Mohammad Ikram Haider - University of Iowa
Yaser Mike Banad - University of Oklahoma
Aleksandar Poleksic - University of Northern Iowa
Hongtao Ding - University of Iowa
Resource Type: Preprint
Publication Details: ArXiv.org
DOI: 10.48550/arxiv.2601.11661
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 01/15/2026
Academic Unit: Mechanical Engineering
Record Identifier: 9985130057502771

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