Building a Cloud-Based Digital Twin for a Robotic Assembly System
Bibliographic record
Abstract
Abstract The potential of Digital Twin (DT) technology to revolutionize industry by enabling virtual simulations of physical systems in real-time has garnered significant attention in recent years. DTs have been widely applied in the manufacturing field to solve various problems, such as shopfloor resource optimization, layout design, commissioning, monitoring, and supervisory control. However, current DT research works are highly conceptual, and how to develop a DT for a legacy system needs more practical understanding. To address the challenge, this paper develops a cloud-based pipeline for building a digital twin of a robotic assembly system. The proposed pipeline is featured by cloud infrastructures, real-time bi-directional monitoring and control, and web-based DT. The use of cloud infrastructure and WebGL helps to increase remote accessibility, enabling real-time visualization of the DT through web environments. To demonstrate the viability of the proposed pipeline, a proof-of-concept DT of a robotic assembly system with over 2000 components was implemented with demonstrated scenes of online order submission, job status monitoring, exception handling, and remote feedback control. Overall, the proposed cloud-based DT pipeline could provide a practical and efficient solution for integrating DT technology into real-world cyber-physical systems. Future works and limitations are also discussed in the end.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".