| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Additive Manufacturing Modeling and Simulation: AM Materials, Processes, and Mechanics
|
| Presentation Title |
Defect-based Fatigue Model for AlSi10Mg Produced by Laser Powder Bed Fusion Process |
| Author(s) |
Avinesh Ojha, Wei-Jen Lai, Ziang Li |
| On-Site Speaker (Planned) |
Avinesh Ojha |
| Abstract Scope |
Defect is inevitable in metal parts built by laser powder bed fusion (L-PBF) process. The size, shape, and location of the defect play critical roles in determining material’s fatigue strength. Due to the random nature of defect in the part, statistical method must be employed for fatigue strength estimation. A defect-based statistical fatigue strength model has been developed and validated for L-PBF AlSi10Mg containing keyhole defects with different size distributions. Artificial defects were also introduced for model validation. The model modified Murakami’s formulation to address material dependence and followed Romano’s approach to consider the statistical behavior of fatigue strength. However, the proposed model is unable to predict fatigue strength of material containing lack-of-fusion defect possibly due to higher stress concentration induced by its morphology. |