| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing Fatigue and Fracture: Developing Predictive Capabilities
|
| Presentation Title |
Comparison of Statistical Predictors of Additive Manufacturing Process-induced Defects Using Fractography and Metallography |
| Author(s) |
David S. Scannapieco, Austin Ngo, Collin Sharpe, Mahya Shahabi, Sneha Narra, John J Lewandowski |
| On-Site Speaker (Planned) |
David S. Scannapieco |
| Abstract Scope |
Additive manufacturing (AM) is often challenged by its large variation in material quality across different process parameters and machines. In some cases (e.g. non-fracture-critical applications), the design freedoms and rapid prototyping afforded by AM can provide immediate implementation opportunities. However, to establish confidence in the material’s quality, particularly for fracture-critical applications, statistical predictors are needed which can determine, to a needed level of confidence, the reliability of a material built in a particular way. This study examines several predictive measures from extreme value analysis and extrapolated regression analysis across datasets from both metallography and a novel fractographical procedure conducted on fatigue-tested samples. Reliability, advantages, disadvantages, and comparison to the current ASTM standards are addressed. Challenges that arise regarding the construction of high confidence intervals, necessary for fatigue-critical applications, are presented for Ti-6Al-4V samples processed in different regimes to purposely create different types of process-induced defects. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Titanium, Other |