Emissivity Prediction of Functionalized Surfaces Using Artificial Intelligence

TL;DR

This paper demonstrates how artificial intelligence can accurately predict the emissivity of complex surfaces, aiding thermal radiation applications by using surface data and fabrication parameters, validated through extensive experimental results.

Contribution

The study introduces AI-based methods for predicting surface emissivity, overcoming challenges of traditional physics-based models and measurement procedures.

Findings

AI accurately predicts emissivity from surface images and parameters.

High agreement between predicted and measured emissivity values.

AI approach expands potential for thermal management applications.

Abstract

The radiative response of any object is governed by a surface parameter known as emissivity. Tuning the emissivity of surfaces has been of great interest in many applications involving thermal radiation such as thermophotovoltaics, thermal management systems, and passive radiative cooling. Although several surface engineering techniques (e.g., surface functionalization) have been pursued to alter the emissivity, there exists a knowledge gap in precisely predicting the emissivity of a surface prior to the modification/fabrication process. Predicting emissivity by a physics-based modeling approach is challenging due to surface's contributing factors, complex interactions and interdependence, and measuring the emissivity requires a tedious procedure for every sample. Thus, a new approach is much-needed to systematically predict the emissivity and expand the applications of thermal radiation. In this work, we demonstrate the immense advantage of employing artificial intelligence (AI) techniques to predict the emissivity of complex surfaces. For this aim, we fabricated 116 bulk aluminum 6061 samples with various surface characteristics using femtosecond laser surface processing (FLSP). A comprehensive dataset was established by collecting surface characteristic data, laser operating parameters, and measured emissivities for all samples. We demonstrated the application of AI in two distinct scenarios. First, the range of emissivity of an unknown sample was shown to be estimated correctly solely based on its 3D surface morphology image. Second, the emissivity of a sample was precisely predicted based on its surface characteristics data and fabrication parameters. The implementation of the AI techniques resulted in the highly accurate prediction of emissivity by showing excellent agreement with the measurements.