Date of Award
12-1-2025
Degree Name
Master of Science
Department
Mechanical Engineering
First Advisor
Jung, Sangjin
Abstract
Metal 3D printing has emerged as a widely used additive manufacturing method, finding applications across numerous industries and research areas. However, its application is often hindered by geometric inaccuracies and surface quality issues resulting from the complex interplay of laser power, melt pool behavior, and cooling characteristics. These inconsistencies have a significant impact on the design and functionality of printed parts, affecting structural integrity and dimensional accuracy. Thus, an effective data-driven approach is crucial to predict these deviations before manufacturing. This study proposes a methodology that incorporates Non-destructive evaluation techniques, like in-situ monitoring and X-ray CT scan, with Conditional Generative Adversarial Networks (cGANs) to predict the geometric deviations on nature-inspired TPMS lattice structures. Additionally, the study also explores strategies to improve the model's accuracy and reliability, ensuring more precise predictions. Using the trained network, accurate images of printed layers are generated that show the regions of concern where the deviations are likely to occur. These predictions provide insight into the nature of deviations and allow for geometric compensation to minimize them prior to the printing process, improving the performance of printed parts in real-world applications.
Access
This thesis is Open Access and may be downloaded by anyone.