About this Abstract |
| Meeting |
MS&T26: Materials Science & Technology
|
| Symposium
|
Computational Materials for Qualification and Certification
|
| Presentation Title |
Robust Manufacturing and Qualification of 3D-Printed Ceramics |
| Author(s) |
Samuel B. Inman, Nathan Bianco, Kimberly Bassett, Patrick Fleig, Garrett Paryznski, A. Sanford, K.A. Martinez, S. Dudley, Remi Dingreville, Brad L. Boyce |
| On-Site Speaker (Planned) |
Samuel B. Inman |
| Abstract Scope |
Emerging manufacturing techniques such as Lithography-Based Ceramic Manufacturing introduce numerous new process variables, creating complex variability that challenges traditional qualification and certification (Q&C) approaches. Traditional methods focus on post-process property evaluation, often too slow and limited in capturing how processing parameters affect part quality. This work addresses the critical need to identify and monitor key processing parameters that significantly influence part quality, enabling their integration into data-driven models to improve the efficiency and robustness of Q&C. By fusing multimodal in-situ process monitoring with high-throughput, non-destructive post-process characterization, we explore optimization of processing parameters and assess sensitivity to drifting or otherwise off-target processing parameters. Variability in part geometry and mechanical properties is evaluated probabilistically to provide statistically robust failure risk estimates and anomaly detection, supporting machine learning frameworks for autonomous process control. This methodology advances computational materials technologies for Q&C by enabling data-driven trust in manufacturing processes within statistically qualified envelopes. |