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.
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http://dx.doi.org/10.1038/s41598-024-80541-9 | DOI Listing |
J Biol Eng
January 2025
Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, 24016, USA.
Extracellular vesicles (EVs) are widely investigated for their implications in cell-cell signaling, immune modulation, disease pathogenesis, cancer, regenerative medicine, and as a potential drug delivery vector. However, maintaining integrity and bioactivity of EVs between Good Manufacturing Practice separation/filtration and end-user application remains a consistent bottleneck towards commercialization. Milk-derived extracellular vesicles (mEVs), separated from bovine milk, could provide a relatively low-cost, scalable platform for large-scale mEV production; however, the reliance on cold supply chain for storage remains a logistical and financial burden for biologics that are unstable at room temperature.
View Article and Find Full Text PDFSci Rep
January 2025
Udmurt Federal Research Center of the Ural Branch of RAS, Baramzina str. 34, Izhevsk, 426067, Russia.
Ultrasound can improve the quality of finished products by reducing porosity and enhancing microstructure in selective laser melting, directed energy deposition, and laser beam welding. This study evaluates the efficiency of ultrasound produced by a pulsed laser via the optoacoustic effect. A quantitative model of collapse of vapor-gas bubbles has been developed under the conditions of ultrasonic treatment at near resonance frequencies.
View Article and Find Full Text PDFInt J Biol Macromol
January 2025
BIOLab Research Group, Department of Chemistry and Industrial Chemistry, University of Pisa, UdR INSTM - Pisa, Via G. Moruzzi 13, 56124 Pisa, Italy. Electronic address:
Polyelectrolyte complexes (PECs) are self-assembled systems formed from oppositely charged polymers, used to create hydrogels for cell culture. This work was aimed at additive manufacturing 3D hydrogels made of a PEC between chitosan (Cs) and alginate, as well as their investigation for in vitro 3D ovarian cancer modeling. PEC hydrogels stability in cell culture medium demonstrated their suitability for long-term cell culture applications.
View Article and Find Full Text PDFJ Occup Environ Hyg
January 2025
Center for Environmental Solutions and Emergency Response, United States Environmental Protection Agency, Cincinnati, Ohio.
Chemical release data are essential for performing chemical risk assessments to understand the potential exposures arising from industrial processes. Often, these data are unknown or unavailable and must be estimated. A case study of volatile organic compound releases during extrusion-based additive manufacturing is used here to explore the viability of various regression methods for predicting chemical releases to inform chemical assessments.
View Article and Find Full Text PDF3D Print Med
January 2025
Department of Surgical & Interventional Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
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