About this Abstract |
| Meeting |
MS&T26: Materials Science & Technology
|
| Symposium
|
Computational Materials for Qualification and Certification
|
| Presentation Title |
An Overview of the OPAL Digital Twin to Predict Fatigue Lives Via a Inputs from Computational Materials Models and In-Situ Sensing |
| Author(s) |
Carl Fauver, James Sobotka, Erin DeCarlo, Guhaprasanna Manogharan, Callie Zawaski, Brendan Croom |
| On-Site Speaker (Planned) |
Carl Fauver |
| Abstract Scope |
In OPAL (One Part And Life), we propose a new part-by-part qualification and certification approach for metal additively manufactured (AM) parts where each One Part uniquely built by an AM process has its own One Life predicted based on its unique defects and microstructure signature. We recognize three major technical gaps. First, we must sense and record process disturbances that form inherent defects and thermal history that govern the microstructure. Second, within a defect and microstructure twin (DMT) tool, we must predict defect formation and microstructure evolution driven by the captured thermal history and other sensor modalities. Third, we must integrate the unique fingerprint from the DMT to improve the accuracy of an efficient, practical, and credible structural integrity framework. This presentation provides an overview of the OPAL effort and describes efforts to validate it via an extensive validation campaign. |