https://scholarworks.unist.ac.kr/handle/201301/63 2026-03-27T06:46:10Z https://scholarworks.unist.ac.kr/handle/201301/90334 Title: Impact of Surrounding Powder on Thermal and Mechanical Behaviors in Powder Bed Fusion Additive Manufacturing Process Simulations Author(s): Kim, Namhun; Lee, SeungJae; Chung Baek, Adrian Matias; Seong, Minkyu; Kim, Taehwan Abstract: Finite element simulation provides a powerful tool for examining the intricate thermal and mechanical behaviors in additive manufacturing processes. By predicting temperature distribution, residual stresses, and deformation, simulations play a critical role in improving part quality and reducing defects. In Powder Bed Fusion (PBF) processes, steep thermal gradients caused by rapid heating and cooling cycles make materials particularly susceptible to residual stresses and warping. While many existing PBF simulations approximate the effect of surrounding unmelted powder using simplified convective boundary conditions, this approach often overlooks the detailed interactions between the powder and the solidified material. This study presents an explicit modeling method for the surrounding powder to investigate its thermal and mechanical interactions with the printed part. By considering the full powder bed in the simulation, we aim to evaluate the effects of the interactions on the process and explore the feasibility of assessing powder reuse potential. The simulation is performed using commercial software (i.e., ANSYS) and the results are validated against experimental data. The proposed approach is expected to enhance the accuracy of PBF process simulations and provide deeper insights into powder behavior and sustainability. 2025-05-31T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/90333 Title: Generative model-based super-resolution framework for improved defect detection in LPBF process Author(s): Kim, Namhun; Chung Baek, Haekwon Adrian; Kim, Taehwan; Seong, Minkyu; Lee, Seungjae Abstract: Recent technological advancements have positioned metal additive manufacturing, particularly laser powder bed fusion (LPBF), as a transformative technology in high-value industries. Despite its potential, challenges in process reliability and reproducibility continue to impede its widespread industrial adoption, underscoring the need for robust in-situ monitoring systems for quality assurance. Although high speed camera-based monitoring systems show promise, the inherent constraints of the LPBF environment and prohibitive costs of advanced sensors often require the use of lower-specification cameras. This limitation in image acquisition significantly impacts the data resolution and thus its applications, particularly in artificial intelligence-based defect detection. This study proposes a novel generative model-based framework comprising enhanced super-resolution and defect detection models. The proposed super-resolution model improves layer-wise images collected from the built-in camera of a commercial LPBF machine by leveraging defect-specific textural features during training. Using the improved images, a subsequent defect detection demonstrates robust performance in identifying process anomalies. Comprehensive quantitative and qualitative analyses are performed to validate the effectiveness of the proposed approach. The results demonstrate substantial improvements in both image resolution and defect detection accuracy, suggesting promising implications for advanced process monitoring and control within LPBF systems. 2025-06-02T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/90313 Title: IDW-ResUNet Framework for Ground-Level Radioactive Contamination Estimation Using UAV-Based Airborne Radiation Data Author(s): Jeon, Seongyoon; Kim, Minjae; Kim, Namhun; Lee, Jeeyon; Kim, Byoungjik 2025-10-29T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/90312 Title: UAV 기반 방사선원 위치 추정에서 비행 고도와 지형 차폐 효과 분석 크리깅 보간법 시뮬레이션 연구 Author(s): Kim, Minjae; Jeon, Seongyoon; Kim, Namhun; Lee, Jeeyeon; Kim, Byongjik 2025-11-26T15:00:00Z