Born Qualified Additive Manufacturing: In-situ Part Quality Assurance in Metal Additive Manufacturing
| dc.contributor.author | Bevans, Benjamin D. | en |
| dc.contributor.committeechair | Rao, Prahalada Krishna | en |
| dc.contributor.committeemember | Johnson, Blake | en |
| dc.contributor.committeemember | Spears, Thomas | en |
| dc.contributor.committeemember | Kong, Zhenyu | en |
| dc.contributor.department | Industrial and Systems Engineering | en |
| dc.date.accessioned | 2024-07-24T08:00:12Z | en |
| dc.date.available | 2024-07-24T08:00:12Z | en |
| dc.date.issued | 2024-07-23 | en |
| dc.description.abstractgeneral | The long-term goal of this dissertation is to develop quality assurance methodologies for parts made using metal additive manufacturing (AM). Additive manufacturing is becoming a prominent manufacturing process due to its ability to generate complex structures that would otherwise be impossible to produce using traditional machining. This freedom of complexity enables engineers to make more efficient components and reduce part counts in assemblies. However, the AM process tends to generate random flaws that require manufacturers to perform extensive testing on all manufactured samples to ensure part quality. Due to this extensive testing, manufacturers have been slow to adopt the AM process. Thus, the goal of this dissertation is to understand, monitor, and predict the quality of metal AM parts as they are being printed to remove the need for post-manufacturing testing – hence the phrase Born Qualified. To enable Born Qualified manufacturing with AM, the objective of this dissertation was to use sensors installed on AM machines to monitor part quality during the process. With this objective, this dissertation focused on: (1) using acoustic signal monitoring to determine the onset of process instabilities that would generate flaws; (2) monitoring the process with multiple sensors to determine the specific type of flaws formed; (3) developing novel methods to monitor the sub-surface effects; and (4) combining multiple streams of sensor data with thermal simulations to detect flaw formation along with mechanical and material properties of the manufactured parts. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:41148 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/120683 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Additive Manufacturing | en |
| dc.subject | Digital Twin | en |
| dc.subject | Heterogeneous Sensing | en |
| dc.subject | Machine Learning | en |
| dc.subject | Quality Assurance | en |
| dc.title | Born Qualified Additive Manufacturing: In-situ Part Quality Assurance in Metal Additive Manufacturing | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Industrial and Systems Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |