Operations Research, Systems Engineering and Industrial Engineering Commons^™

Deep Multi-Modal U-Net Fusion Methodology Of Infrared And Ultrasonic Images For Porosity Detection In Additive Manufacturing, Christian E. Zamiela

Theses and Dissertations

We developed a deep fusion methodology of non-destructive (NDT) in-situ infrared and ex- situ ultrasonic images for localization of porosity detection without compromising the integrity of printed components that aims to improve the Laser-based additive manufacturing (LBAM) process. A core challenge with LBAM is that lack of fusion between successive layers of printed metal can lead to porosity and abnormalities in the printed component. We developed a sensor fusion U-Net methodology that fills the gap in fusing in-situ thermal images with ex-situ ultrasonic images by employing a U-Net Convolutional Neural Network (CNN) for feature extraction and two-dimensional object localization. We …