| Abstract Scope |
This presentation introduces an Additive Manufacturing defect model, a rapid, physics-based, probabilistic framework designed to predict defects in laser powder bed fusion processes. Grounded in mechanistic insights, the model uses closed-form analytical equations at the micro-scale combined with numerical calculations at the part-level. This model offers computational acceleration significantly surpassing conventional methods, predicting location-specific probabilities of defect formation—including size and volume density—based on local thermal paths. It integrates seamlessly into existing workflows across design, structures, lifing, manufacturing, and quality control sectors. The focus of the talk will be on detailing this modeling approach and its specific application to the Ti64 alloy. Calibration and validation efforts for Ti-6Al-4V, demonstrating the model’s efficacy and accuracy, will also be discussed. |