Abstract Additive manufacturing (AM) has emerged as a revolutionary manufacturing technology that offers significant benefits. However, the qualification of AM materials, processes, components, and machines remains a major challenge, while rigorous certification is indispensable for deployment, particularly in aerospace. Conventional qualification methods relying on extensive experimental testing are time-consuming and costly, due to the microstructural complexity of AM-printed materials and the demanding tailored AM processing parameters. In this study, we present an Integrated Computational Materials Engineering (ICME)-based rapid qualification framework for the post-printing heat treatment of AM ATI 718Plus® alloy. This ICME framework integrates calibrated microstructure evolution models and a yield strength property model to establish 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. Through a linear-transformation based statistical calibration approach, the predicted yield strength distribution agrees very well with experimental results, thus enabling rapid qualification through the estimation of strength distribution, including the 1% minimum and 0.3% minimum (3σ) yield strength values. This approach substantially reduces the time and cost of qualification, demonstrating that reliable certification can be achieved with as few as 18 experimental data points for the 1% minimum value and 21 for the 0.3% minimum (3σ) value.
Zhang et al. (Mon,) studied this question.