Engineering Science and Materials Commons^™

Emissivity Prediction Of Functionalized Surfaces Using Artificial Intelligence, Greg Acosta, Andrew Reicks, Miguel Moreno, Alireza Borjali, Craig Zuhlke, Mohammad Ghashami

Department of Mechanical and Materials Engineering: Faculty Publications

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 …