This Engineering Research Initiation (ERI) award will strengthen metal additive manufacturing by reducing defect-driven scrap and rework in laser powder bed fusion, a process used to produce geometrically complex metal parts for aerospace, biomedical, and energy systems. A common reason of build failure is the formation of streaks when the powder spreading recoater damages or disturbs. These streaks can trigger porosity and incomplete melting that propagate to later layers, degrading reliability and increasing cost, energy use, and material waste. This project will create a practical, in-process quality-control capability that observes each layer and applies the smallest safe corrective action only where it is needed. By turning layer images into risk-aware interventions, this work will advance the national interest by promoting the progress of metal additive manufacturing, supporting a more resilient industrial base through higher yield and less waste. Results will be integrated into course modules and laboratory exercises that train students in data-centric manufacturing. Outreach with regional manufacturers and community colleges will expand participation in manufacturing education and training and accelerate adoption of modern quality practices. The technical goal of this project is to develop a within-layer detect-predict-decide-act loop that couples perception, modeling, and control under explicit safety and time limits. A lightweight vision model will segment and quantify recoater streaks on each layer in no more than one tenth of a second and will output geometry features with calibrated confidence. A layer-aware digital twin, implemented as a fast hybrid surrogate maps streak features, scan plan, and energy/cooling descriptors to a calibrated porosity-risk map and exposes a what-if interface that scores candidate repairs by predicted risk reduction and time cost. A minimal-intervention controller will then select among a compact action set, such as micr