An integrated computational framework for uncertainty quantification and rapid qualification of heat treatment for additively manufactured ATI 718Plus alloy

Additive manufacturing (AM) has emerged as a revolutionary manufacturing technology offering significant benefits, though its qualification remains a major challenge, especially in aerospace applications where rigorous certification is crucial. This study presents an Integrated Computational Materials Engineering (ICME)-based rapid qualification framework for the post-print heat treatment of AM ATI 718Plus® alloy. The proposed framework incorporates calibrated microstructure evolution models and a yield strength property model to create a process-structure-property (PSP) linkage under various heat treatment conditions. Over 2000 Monte Carlo-sampled virtual scenarios were simulated to capture the microstructural and property variability effectively. A linear-transformation statistical calibration approach was utilized, revealing that the predicted yield strength distribution aligns closely with experimental results. This innovative method enables rapid qualification, allowing for yield strength estimation—including critical values like the 1% minimum and 0.3% minimum (3σ)—with a substantially reduced number of experimental data points. Specifically, just 18 data points were required for the 1% minimum and 21 for the 0.3% minimum (3σ) value. The study demonstrates that reliable certification can be achieved more efficiently, significantly cutting costs and time associated with the qualification process.