Exploration of surface roughness measurement solutions for additive manufactured components built by multi-axis tool paths AITranslate
Abstract AITranslate
A thin-wall sphere dome set is manufactured without supports being required using a direct energy deposition additive manufacturing process. A multi-axis tool path combined with a partitioning strategy is utilized. Two partitioning strategies and a rotary toolpath build strategy are applied. The partitioned samples have a 45 mm diameter, and the rotary toolpath sample has a 60 mm diameter. The wall thickness is 2 mm for all case studies. The partitioning strategy provides an opportunity to fabricate geometries without the need for support structures, but it introduces physical properties variations. Large surface irregularities are noticed at the transition points between partitions, which escalates the roughness values drastically in these regions. The surface roughness variations within a build section and across partitions are explored in this paper. Process planning, data collection, and experimental/numerical procedures are developed and implemented to investigate the surface roughness variations (Ra measurement). Two solutions are developed: (i) a destructive testing approach (sections embedded into epoxy) and (ii) a contactless approach. The destructive testing solution uses the magnified pictures of the exposed surface edges of mount samples as input data. The other solution uses a 3D point cloud of the surface. The mount solution results show better accuracy, but the data collection process is time consuming and labour intensive. The destructive test method can detect small textures, while the contactless method is more appropriate for large-scale bead products. This research shows measuring surface roughness is challenging and further development is needed to be developed to be able to effectively measure Ra for AM built components.
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DOI:https://doi.org/10.1016/j.addma.2020.101822
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A thin-wall sphere dome set is manufactured without supports being required using a direct energy deposition additive manufacturing process. A multi-axis tool path combined with a partitioning strategy is utilized. Two partitioning strategies and a rotary toolpath build strategy are applied. The partitioned samples have a 45 mm diameter, and the rotary toolpath sample has a 60 mm diameter. The wall thickness is 2 mm for all case studies. The partitioning strategy provides an opportunity to fabricate geometries without the need for support structures, but it introduces physical properties variations. Large surface irregularities are noticed at the transition points between partitions, which escalates the roughness values drastically in these regions. The surface roughness variations within a build section and across partitions are explored in this paper. Process planning, data collection, and experimental/numerical procedures are developed and implemented to investigate the surface roughness variations (Ra measurement). Two solutions are developed: (i) a destructive testing approach (sections embedded into epoxy) and (ii) a contactless approach. The destructive testing solution uses the magnified pictures of the exposed surface edges of mount samples as input data. The other solution uses a 3D point cloud of the surface. The mount solution results show better accuracy, but the data collection process is time consuming and labour intensive. The destructive test method can detect small textures, while the contactless method is more appropriate for large-scale bead products. This research shows measuring surface roughness is challenging and further development is needed to be developed to be able to effectively measure Ra for AM built components.
quote
| GB/T 7714-2015 | [1] Hamed Kalami, Jill Urbanic. Additive Manufacturing, 2021(38). DOI:10.1016/j.addma.2020.101822. |
| MLA | [1] Hamed Kalami, and Jill Urbanic. Additive Manufacturing, no. 38, 2021, https://doi.org/10.1016/j.addma.2020.101822. |
| APA | [1] Hamed Kalami, & Jill Urbanic. (2021). Additive Manufacturing(38). https://doi.org/10.1016/j.addma.2020.101822 |
| IEEE | [1] Hamed Kalami and Jill Urbanic, Additive Manufacturing, no. 38, 2021, doi: 10.1016/j.addma.2020.101822. |
