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

2026-01-15

AI Summary

Accurately predicting the wettability of laser-textured metal alloy surfaces is critical for applications such as heat transfer and lubrication, as it is jointly governed by surface topography and chemistry. This work proposes a machine learning framework that, for the first time, integrates multiscale morphological features—derived from Laws’ texture energy and profilometry data—with chemical descriptors, including functional group polarity and molecular volume extracted via X-ray photoelectron spectroscopy (XPS). By employing an ensemble neural network architecture incorporating residual connections, batch normalization, and dropout, the model achieves high-accuracy contact angle prediction (R² = 0.942, RMSE = 13.896), substantially outperforming existing approaches. The analysis further reveals the dominant influence of chemical features, demonstrating the potential of AI-driven design of functional surfaces.

Technology Category

Machine Learning: Feature Construction/ReformulationNatural Language Processing: Language Grounding & Multi-modal NLPIntelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsWeb Mining and Content Analysis: Machine learning and data science for the WebResponsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• surface wettability
• laser texturing
• metal alloys
• contact angle prediction
• surface chemistry

Innovation

• machine learning
• surface wettability
• laser texturing
• ensemble neural network
• feature importance