Abstract
This study proposes a Bayesian inference-based decision framework to quantify the physical uncertainty based on fatigue life tests on maraging steel according to post-processing treatments and build orientations. Uncertainty quantification of fatigue life models is performed to determine the most suitable models for the metal additive manufacturing process by employing Bayesian inference. To select one of the fatigue life models, we introduce a weighted-equivalent metric (WEM) to compare the evaluation results from different statistical metrics. By evaluating the WEM value, the logistic model and Zhurkov fatigue life model are identified as the suitable fatigue life models for maraging steel.
| Original language | English |
|---|---|
| Article number | 106535 |
| Journal | International Journal of Fatigue |
| Volume | 155 |
| DOIs | |
| State | Published - Feb 2022 |
| Externally published | Yes |
Funding
The authors would like to thank the funding agency, NAMIC (National Additive Manufacturing and Innovation Center) through Grant No. 2017295 and Industrial Partner - ST Engineering Land Systems, Singapore for their in-kind contributions. Authors acknowledge the efforts of Senior Specialist Mr. Kai Lee Tan, Digital Manufacturing and Design (DManD) Centre, SUTD, Singapore, for assistance with specimen fabrication and testing. David Rosen and Nagarajan Raghavan would like to further thank the funding provided by the A*STAR Advanced Manufacturing and Engineering (AME) Industry Alignment Fund (Grant No. A19E1a0097). Furthermore, this research was also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1I1A3044394, PI: Prof. Jaehyeok Doh) and the Research Grant of Jeonju University in 2020 (PI: Prof. Samyeon Kim).
Keywords
- Bayesian inference
- Fatigue life model
- Metal additive manufacturing
- Uncertainty quantification
- Weighted-Equivalent Metric (WEM)
