Emissivity prediction of functionalized surfaces using artificial intelligence

Authors

Greg Acosta, University of Nebraska-LincolnFollow
Andrew Reicks, University of Nebraska-LincolnFollow
Miguel Moreno, University of Nebraska-Lincoln
Alireza Borjali, Harvard Medical School
Craig Zuhlke, University of Nebraska-LincolnFollow
Mohammad Ghashami, University of Nebraska - LincolnFollow

Date of this Version

2022

Citation

Published in Journal of Quantitative Spectroscopy & Radiative Transfer 291 (2022) 108325

doi:10.1016/j.jqsrt.2022.108325

Comments

Copyright © 2022 Elsevier Ltd. Used by permission.

Abstract

Tuning surface emissivity has been of great interest in thermal radiation applications, such as thermophotovoltaics and passive radiative cooling. As a low-cost and scalable technique for manufacturing surfaces with desired emissivities, femtosecond laser surface processing (FLSP) has recently drawn enormous attention. Despite the versatility offered by FLSP, there is a knowledge gap in accurately predicting the outcome emissivity prior to fabrication. 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 used FLSP to fabricate 116 different aluminum samples. A comprehensive dataset was established by collecting surface characteristics, laser operating parameters, and the measured emissivities for all samples. We demonstrate the successful application of AI in two distinct scenarios: (1) effective emissivity classification solely based on 3D surface morphology images, and (2) emissivity prediction based on surface characteristics and FLSP parameters. These findings open new pathways towards extended implementation of AI to predict various surface properties in functionalized samples or extract the required fabrication parameters via reverse engineering.