| Abstract Scope |
Additive manufacturing (AM) qualification remains a barrier to broader adoption of metal AM in aerospace and defense. Traditional material allowables frameworks assume material homogeneity and statistical stability, assumptions that do not hold for AM processes where local material properties depend on time‑resolved thermal and process history. As a result, qualification often requires extensive testing while still providing limited insight into spatially varying part performance.
This presentation describes a risk‑informed framework for an AI‑enabled Digital Twin to support qualification of metal AM processes, demonstrated using Laser Metal Deposition with wire (LMD‑w). The framework leverages process log data together with inspection and mechanical test outcomes to enable uncertainty‑aware assessment of material performance and defect risk. Rather than replacing existing qualification practices, the Digital Twin is positioned as a decision‑support tool that supports traceability, quantifies uncertainty, and enables risk‑based determination of testing sufficiency. |