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.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-6391 |
| Date | 10 December 2021 |
| Creators | Zamiela, Christian E |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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