Spreading Process Maps for Powder-Bed Additive Manufacturing Derived from Physics Model-Based Machine Learning
| dc.citation.articleNumber | 1176 | en_US |
| dc.citation.issueNumber | 11 | en_US |
| dc.citation.journalTitle | Metals | en_US |
| dc.citation.volumeNumber | 9 | en_US |
| dc.contributor.author | Desai, Prathamesh S. | en_US |
| dc.contributor.author | Higgs, C. Fred III | en_US |
| dc.date.accessioned | 2020-02-14T16:39:47Z | en_US |
| dc.date.available | 2020-02-14T16:39:47Z | en_US |
| dc.date.issued | 2019 | en_US |
| dc.description.abstract | The powder bed additive manufacturing (AM) process is comprised of two repetitive steps—spreading of powder and selective fusing or binding the spread layer. The spreading step consists of a rolling and sliding spreader which imposes a shear flow and normal stress on an AM powder between itself and an additively manufactured substrate. Improper spreading can result in parts with a rough exterior and porous interior. Thus it is necessary to develop predictive capabilities for this spreading step. A rheometry-calibrated model based on the polydispersed discrete element method (DEM) and validated for single layer spreading was applied to study the relationship between spreader speeds and spread layer properties of an industrial grade Ti-6Al-4V powder. The spread layer properties used to quantify spreadability of the AM powder, i.e., the ease with which an AM powder spreads under a set of load conditions, include mass of powder retained in the sampling region after spreading, spread throughput, roughness of the spread layer and porosity of the spread layer. Since the physics-based DEM simulations are computationally expensive, physics model-based machine learning, in the form of a feed forward, back propagation neural network, was employed to interpolate between the highly nonlinear results obtained by running modest numbers of DEM simulations. The minimum accuracy of the trained neural network was 96%. A spreading process map was generated to concisely present the relationship between spreader speeds and spreadability parameters. | en_US |
| dc.identifier.citation | Desai, Prathamesh S. and Higgs, C. Fred III. "Spreading Process Maps for Powder-Bed Additive Manufacturing Derived from Physics Model-Based Machine Learning." <i>Metals,</i> 9, no. 11 (2019) MDPI: https://doi.org/10.3390/met9111176. | en_US |
| dc.identifier.digital | metals-09-01176 | en_US |
| dc.identifier.doi | https://doi.org/10.3390/met9111176 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/108046 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | MDPI | en_US |
| dc.rights | This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.subject.keyword | powder-bed additive manufacturing (AM) | en_US |
| dc.subject.keyword | powder spreading | en_US |
| dc.subject.keyword | spreading process map | en_US |
| dc.subject.keyword | discrete element method (DEM) | en_US |
| dc.subject.keyword | machine learning | en_US |
| dc.title | Spreading Process Maps for Powder-Bed Additive Manufacturing Derived from Physics Model-Based Machine Learning | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
Files
Original bundle
1 - 1 of 1