The University of North Florida has received a grant from the U.S. National Science Foundation to develop a real-time quality control system for laser powder bed fusion (LPBF) additive manufacturing, aiming to address common defect issues in the process. In LPBF, parameter fluctuations, uneven powder spreading, and thermal stress can lead to defects such as porosity, lack of fusion, and cracks, compromising the mechanical properties and reliability of final parts. This research project seeks to integrate advanced in-situ monitoring technologies, sensor data fusion, and artificial intelligence algorithms to achieve real-time perception, analysis, and control of key physical phenomena during the printing process. The goal is to build an intelligent closed-loop control system that can predict and proactively intervene to prevent defect formation, thereby enhancing the stability, repeatability, and yield of LPBF processes, and promoting the reliable application of this technology for direct production of critical load-bearing components.