Abstract
A probabilistic model to estimate the fatigue life of an additively manufactured material with polished surface was developed based on the statistical size distribution of grains and various manufacturing defects such as pores and unfused powder particles embedded in the microstructure. The probabilistic model includes the prospect of each microstructural feature to exist on the specimen surface and its potential impact on fatigue crack initiation through defect-grain interaction. The model was applied to as-built and peak-hardened Al-1.5Cu-0.8Sc-0.4Zr (wt.%) alloy developed by laser powder bed fusion. Bending fatigue experiments performed on both as-built and peak-hardened samples confirmed the trends predicted by the probabilistic model. The cumulative probability distribution plotted against fatigue life fitted well to a three-parameter Weibull distribution function. The indicator for the scatter in fatigue life from the Weibull fit suggested that peak hardening of the material narrowed fatigue life bounds. The probabilistic model was further used as a predictive tool to estimate fatigue life for a preferred microstructure with a lower pore number density and pore size.
| Original language | English |
|---|---|
| Article number | 140082 |
| Journal | Materials Science and Engineering: A |
| Volume | 798 |
| DOIs | |
| State | Published - Nov 4 2020 |
| Externally published | Yes |
Funding
This work is based on research sponsored by the Office of Naval Research under the ONR Award # N00014-17-1-2559 . The authors are very grateful to Prof. Yongho Sohn and Dr. Le Zhou for providing the printed block. We thank the Materials Research Facility (MRF) and Advanced Materials and Manufacturing Processes Institute (AMMPI) at University of North Texas for access to Scanning electron microscopy and X-ray microscopy facilities, respectively. We are also grateful to Michael Toll, Haider Janjua and Advika Chesetti for helping in polishing the mini fatigue samples. This work is based on research sponsored by the Office of Naval Research under the ONR Award #N00014-17-1-2559. The authors are very grateful to Prof. Yongho Sohn and Dr. Le Zhou for providing the printed block. We thank the Materials Research Facility (MRF) and Advanced Materials and Manufacturing Processes Institute (AMMPI) at University of North Texas for access to Scanning electron microscopy and X-ray microscopy facilities, respectively. We are also grateful to Michael Toll, Haider Janjua and Advika Chesetti for helping in polishing the mini fatigue samples.
| Funders | Funder number |
|---|---|
| Materials Research Facility | |
| Office of Naval Research | N00014-17-1-2559 |
Keywords
- Additive manufacturing
- Al alloys
- Fatigue
- Porosity
- Probabilistic model
