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

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