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Deep Multi-Modal U-Net Fusion Methodology Of Infrared And Ultrasonic Images For Porosity Detection In Additive Manufacturing, Christian E. Zamiela
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 …
Sensor Data Based Adaptive Models For Assembly Worker Training In Cyber Manufacturing, Md. Al-Amin
Sensor Data Based Adaptive Models For Assembly Worker Training In Cyber Manufacturing, Md. Al-Amin
Doctoral Dissertations
“Production innovations are occurring faster than ever leading conventional production systems towards cyber manufacturing. Manufacturing workers thus need to frequently learn new methods and skills. In fast-changing, largely uncertain production systems, manufacturers with the ability to comprehend workers’ behavior and assess their operational performance in near real-time will achieve better performance than peers. Recognizing worker actions in near real-time while performing the assembly can serve this purpose. However, reliably recognizing the assembly actions performed by the workers is challenging, because the actions for assembly are complex and workers are not only heterogeneous but sensitive to the variation of the work …