Defect Identification and Mitigation Via Visual Inspection in Large-Scale Additive Manufacturing
| Autor: | Brian K. Post, Katherine T. Gaul, Alex Roschli, Michael Borish, Phillip C. Chesser, Lonnie J. Love |
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| Rok vydání: | 2018 |
| Předmět: |
Engineering drawing
Computer science media_common.quotation_subject Scale (chemistry) 0211 other engineering and technologies General Engineering 02 engineering and technology Oak Ridge National Laboratory 021001 nanoscience & nanotechnology Object (computer science) Visual inspection Identification (information) Control system General Materials Science Quality (business) 0210 nano-technology Representation (mathematics) 021102 mining & metallurgy media_common |
| Zdroj: | JOM. 71:893-899 |
| ISSN: | 1543-1851 1047-4838 |
| DOI: | 10.1007/s11837-018-3220-6 |
| Popis: | Defect identification and mitigation is an important avenue of research to improve the overall quality of objects created using additive manufacturing (AM) technologies. Identifying and mitigating defects takes on additional importance in large-scale, industrial AM. In large-scale AM, defects that result in failed prints are extremely costly in terms of time spent and material used. To address these issues, researchers at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility investigated the use of a laser profilometer and thermal camera to collect data concerning an object as it was constructed. These data provided feedback for an in situ control system to adjust object construction. Adjustments were made in the form of automated height control. This paper presents results for both a polymer- and metal-based system. Object construction for both systems was improved significantly, and the resulting objects were more geometrically identical to the ideal 3D representation. |
| Databáze: | OpenAIRE |
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