| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Computational Materials for Qualification and Certification
|
| Presentation Title |
Data-Driven Process Uncertainty Analysis of Stochastic Lack-of-Fusion in Laser Powder Bed Fusion |
| Author(s) |
Vamsi Subraveti, Caglar Oskay |
| On-Site Speaker (Planned) |
Vamsi Subraveti |
| Abstract Scope |
Prediction of uncertain process-induced defects is a critical issue for qualification and certification of parts manufactured via laser powder bed fusion. Stochastic lack-of-fusion porosity (sLOF) is a defect in LPBF that is deleterious to fatigue life. This work integrates sLOF defects into microstructure process simulations to analyze uncertainties in the sLOF volume fraction. An unmelted phase tracking method captures sLOF during a process simulation. Melt pool fluctuations in experimental images were statistically reproduced using a spectral matching algorithm; these fluctuation histories were inputted to the process simulations to generate sLOF predictions. The sLOF prediction model was calibrated using experimental data; sLOF defect populations were generated in RVE simulations. The variation in the sLOF volume fraction was quantified across the process space. This work develops a method to quantify the impact of LBPF process uncertainties on sLOF volume fraction, which is critical to qualification and certification of LPBF parts. |