ORNL Uses Computer Vision and Digital Twins to Improve Large-Scale 3D Printing
May 21, 2026 — Researchers at the U.S. Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) have created a new tool that can catch and correct potential mistakes in real time while 3D printing large plastic parts. The automated system could help U.S. manufacturers produce large, custom parts with fewer defects, potentially reducing waste, lowering […] The post ORNL Uses Computer Vision and Digital Twins to Improve Large-Scale 3D Printing appeared first on HPCwire .
Advances in computer vision and AI processing power have converged, making real-time defect detection and correction in complex manufacturing processes like large-scale 3D printing feasible and economically attractive.
This development represents a significant step towards industrializing additive manufacturing for critical, large-scale components, addressing key limitations of quality control and waste that have hindered broader adoption.
The ability to ensure higher quality and reduce defects in large-scale 3D printing in real-time makes additive manufacturing more reliable and cost-effective for industries requiring custom and complex parts.
- · Additive manufacturing industry
- · Aerospace and defense manufacturing
- · Custom parts manufacturers
- · Industrial software providers
- · Traditional manufacturing of large complex parts
- · Companies reliant on outsourced defect inspection
Increased adoption of large-scale 3D printing across various industrial sectors due to improved reliability and reduced waste.
Decentralization of complex manufacturing as custom parts can be reliably produced closer to the point of need.
New supply chain structures emerge, favoring agile, localized production using advanced manufacturing techniques over centralized mass production.
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