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This method leverages advanced computational modeling to evaluate localized heating conditions and fusion metrics and quantify defect risks dynamically, allowing for optimized build files and process adjustments that drastically improve the final component. The model can be trained using PPF and print samples from an AM machine's prior builds to learn correlations between the machine's instructions, behavior during build, and final product quality. By integrating machine feedback metrics, model-based thermal and fusion metrics, and in-situ sensor metrics, the model learns predictive signatures that allow it to quantify localized defect probability in PBF laser beam metals. Manufacturers can employ this method to understand the reproducibility of their prints and perform defect compensation before parts are fully deployed, improving quality, reliability, and success. Particularly useful to industries that require stringent certification of safety-critical components, such as the aerospace, space, medical, and automotive sectors, this method can be flexibly deployed globally or adapted to specific AM machines. By predicting the probability of defects before and during production, 3D printing service providers and AM equipment manufacturers can save significant amounts of time and money while drastically reducing part variability. This predictive capability also allows organizations to certify parts faster and ensure consistent material properties, which is essential for meeting rigorous performance and safety standards. This is method is currently available for patent licensing (no software included).