Abstract
Additive manufacturing usually involves the complex interaction of design, materials, and manufacturing, often resulting in long and cost-intensive iterative evaluation cycles. Therefore, it is critical for it to be aligned with the Materials Genome Initiative to develop, produce, and deploy high-throughput components. Recognizing a need, this study leverages a feature-based qualification (FBQ) methodology to decompose a complex structure by identifying critical performance-limiting features, for the purpose of reducing the cost and time of DED process qualification. A hybrid-physics-based multi-objective optimization tool was used to predict processing-structure-property relationships in thin-walled builds. The probabilistic ML models achieved targeted predictions with half the sample space when compared with conventional DOEs, while also being 37–50% more reliable with respect to regression tools with linear basis function. Although the current model developed is specific to Ti64 builds in a RPMi557 powder-feed DED machine, the FBQ methodology may be more universally employed to other material-modality combinations.
| Original language | English |
|---|---|
| Pages (from-to) | 3064-3081 |
| Number of pages | 18 |
| Journal | JOM |
| Volume | 73 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 2021 |
| Externally published | Yes |
Funding
The authors would like to thank Mark Benedict and Thomas Broderick at AFRL, and Dave Siddle and Brandon Ribic at America Makes for their continued support and guidance throughout the course of this project. Furthermore, valuable insights and consultations for conducting stress simulations, DOE setup and Bayesian hybrid model creation is much appreciated from Ryan Hurley at EWI (now at DMG Mori), Genghis Khan, Changjie Sun and Sathya Raghavan at GE Research, and Chris Williams at GE Aviation. Author SN acknowledges sample preparation and characterization support from Anthony Poli, Jeremiah Faulkner, Ian Spinelli, Anjali Singhal, Rebecca Casey, Tracy Paxon and Yan Gao at GE Research, and John Sosa at MIPAR Image Analyses. ® This effort was performed through the National Center for Defense Manufacturing and Machining under the America Makes Program entitled Maturation of Advanced Manufacturing for Low-Cost Sustainment, and is based on research sponsored by the Air Force Research Laboratory under agreement number FA8650-16-2-5700. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. The authors would like to thank Mark Benedict and Thomas Broderick at AFRL, and Dave Siddle and Brandon Ribic at America Makes for their continued support and guidance throughout the course of this project. Furthermore, valuable insights and consultations for conducting stress simulations, DOE setup and Bayesian hybrid model creation is much appreciated from Ryan Hurley at EWI (now at DMG Mori), Genghis Khan, Changjie Sun and Sathya Raghavan at GE Research, and Chris Williams at GE Aviation. Author SN acknowledges sample preparation and characterization support from Anthony Poli, Jeremiah Faulkner, Ian Spinelli, Anjali Singhal, Rebecca Casey, Tracy Paxon and Yan Gao at GE Research, and John Sosa at MIPAR? Image Analyses. This effort was performed through the National Center for Defense Manufacturing and Machining under the America Makes Program entitled Maturation of Advanced Manufacturing for Low-Cost Sustainment, and is based on research sponsored by the Air Force Research Laboratory under agreement number FA8650-16-2-5700. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon.
| Funders | Funder number |
|---|---|
| GE Research | |
| National Center for Defense Manufacturing and Machining | |
| Tracy Paxon and Yan Gao at GE Research | |
| U.S. Department of Energy | |
| Air Force Research Laboratory | FA8650-16-2-5700 |
| Air Force Research Laboratory |
