Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Bian, Linkan
Committee Member
Tian, Wenmeng
Committee Member
Wang, Haifeng
Committee Member
Falls, Terril C.
Date of Degree
12-10-2021
Document Type
Graduate Thesis - Open Access
Major
Industrial Engineering
Degree Name
Master of Science (M.S.)
College
James Worth Bagley College of Engineering
Department
Department of Industrial and Systems Engineering
Abstract
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 modify the U-Net framework with the inception and LSTM block layers. We validate the models by comparing our single modality models and fusion models with ground truth X-ray computed tomography images. The inception U-Net fusion model localized porosity with the highest mean intersection over union score of 0.557.
Sponsorship
ERDC
Recommended Citation
Zamiela, Christian E., "Deep multi-modal U-net fusion methodology of infrared and ultrasonic images for porosity detection in additive manufacturing" (2021). Theses and Dissertations. 5313.
https://scholarsjunction.msstate.edu/td/5313
Included in
Industrial Engineering Commons, Operational Research Commons, Systems Engineering Commons