Enabling additive manufacturing part inspection of digital twins via collaborative virtual reality
Abstract Digital twins (DTs) are an emerging capability in additive manufacturing (AM), set to revolutionize design optimization, inspection, in situ monitoring, and root cause analysis. AM DTs typically incorporate multimodal data streams, ranging from machine toolpaths and in-process imaging to X-...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80541-9 |
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| author | Vuthea Chheang Saurabh Narain Garrett Hooten Robert Cerda Brian Au Brian Weston Brian Giera Peer-Timo Bremer Haichao Miao |
| author_facet | Vuthea Chheang Saurabh Narain Garrett Hooten Robert Cerda Brian Au Brian Weston Brian Giera Peer-Timo Bremer Haichao Miao |
| author_sort | Vuthea Chheang |
| collection | DOAJ |
| description | Abstract Digital twins (DTs) are an emerging capability in additive manufacturing (AM), set to revolutionize design optimization, inspection, in situ monitoring, and root cause analysis. AM DTs typically incorporate multimodal data streams, ranging from machine toolpaths and in-process imaging to X-ray CT scans and performance metrics. Despite the evolution of DT platforms, challenges remain in effectively inspecting them for actionable insights, either individually or in a multidisciplinary, geographically distributed team setting. Quality assurance, manufacturing departments, pilot labs, and plant operations must collaborate closely to reliably produce parts at scale. This is particularly crucial in AM where complex structures require a collaborative and multidisciplinary approach. Additionally, the large-scale data originating from different modalities and their inherent 3D nature pose significant hurdles for traditional 2D desktop-based inspection methods. To address these challenges and increase the value proposition of DTs, we introduce a novel virtual reality (VR) framework to facilitate collaborative and real-time inspection of DTs in AM. This framework includes advanced features for intuitive alignment and visualization of multimodal data, visual occlusion management, streaming large-scale volumetric data, and collaborative tools, substantially improving the inspection of AM components and processes to fully exploit the potential of DTs in AM. |
| format | Article |
| id | doaj-art-fc8274a32c8545c78e34a4d65bb2250f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-fc8274a32c8545c78e34a4d65bb2250f2024-12-01T12:26:24ZengNature PortfolioScientific Reports2045-23222024-11-0114111110.1038/s41598-024-80541-9Enabling additive manufacturing part inspection of digital twins via collaborative virtual realityVuthea Chheang0Saurabh Narain1Garrett Hooten2Robert Cerda3Brian Au4Brian Weston5Brian Giera6Peer-Timo Bremer7Haichao Miao8Lawrence Livermore National LaboratoryLawrence Livermore National LaboratoryLawrence Livermore National LaboratoryLawrence Livermore National LaboratoryLawrence Livermore National LaboratoryLawrence Livermore National LaboratoryLawrence Livermore National LaboratoryLawrence Livermore National LaboratoryLawrence Livermore National LaboratoryAbstract Digital twins (DTs) are an emerging capability in additive manufacturing (AM), set to revolutionize design optimization, inspection, in situ monitoring, and root cause analysis. AM DTs typically incorporate multimodal data streams, ranging from machine toolpaths and in-process imaging to X-ray CT scans and performance metrics. Despite the evolution of DT platforms, challenges remain in effectively inspecting them for actionable insights, either individually or in a multidisciplinary, geographically distributed team setting. Quality assurance, manufacturing departments, pilot labs, and plant operations must collaborate closely to reliably produce parts at scale. This is particularly crucial in AM where complex structures require a collaborative and multidisciplinary approach. Additionally, the large-scale data originating from different modalities and their inherent 3D nature pose significant hurdles for traditional 2D desktop-based inspection methods. To address these challenges and increase the value proposition of DTs, we introduce a novel virtual reality (VR) framework to facilitate collaborative and real-time inspection of DTs in AM. This framework includes advanced features for intuitive alignment and visualization of multimodal data, visual occlusion management, streaming large-scale volumetric data, and collaborative tools, substantially improving the inspection of AM components and processes to fully exploit the potential of DTs in AM.https://doi.org/10.1038/s41598-024-80541-9Virtual realityCollaborative VRDigital twinsAdditive manufacturingVirtual inspection |
| spellingShingle | Vuthea Chheang Saurabh Narain Garrett Hooten Robert Cerda Brian Au Brian Weston Brian Giera Peer-Timo Bremer Haichao Miao Enabling additive manufacturing part inspection of digital twins via collaborative virtual reality Scientific Reports Virtual reality Collaborative VR Digital twins Additive manufacturing Virtual inspection |
| title | Enabling additive manufacturing part inspection of digital twins via collaborative virtual reality |
| title_full | Enabling additive manufacturing part inspection of digital twins via collaborative virtual reality |
| title_fullStr | Enabling additive manufacturing part inspection of digital twins via collaborative virtual reality |
| title_full_unstemmed | Enabling additive manufacturing part inspection of digital twins via collaborative virtual reality |
| title_short | Enabling additive manufacturing part inspection of digital twins via collaborative virtual reality |
| title_sort | enabling additive manufacturing part inspection of digital twins via collaborative virtual reality |
| topic | Virtual reality Collaborative VR Digital twins Additive manufacturing Virtual inspection |
| url | https://doi.org/10.1038/s41598-024-80541-9 |
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