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

TL;DR

This paper develops a machine learning framework that accurately predicts the wettability of laser-textured metal alloys based on morphological and chemical surface features, aiding the design of functional surfaces.

Contribution

It introduces an ensemble neural network model that integrates surface morphology and chemistry features to predict wettability with high accuracy, surpassing previous methods.

Findings

Model achieved R2 = 0.942 and RMSE = 13.896 in wettability prediction.

Surface chemistry features have the strongest influence on contact angle.

The approach demonstrates AI's potential in designing tailored surface functionalities.

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.