Toward a systematic reporting framework for Digital Twins: a cooperative robotics case study
Bibliographic record
Abstract
Digital Twins (DTs) can be constructed for many different applications, leading to substantial differences between different case studies. To be able to learn from the challenges and lessons learned by other DT practitioners, it is important that experience reports be consistent to facilitate comparisons. In this paper, we merge three reference description frameworks for DTs, one generated from a systematic mapping study, one generated from an analysis of experience reports, and one from a systematic literature review, to come up with a unified characterization of DT applications. This analysis has identified six non-overlapping and three cross-cutting characteristics in the reference frameworks. This paper showcases the unified characterization with 21 characteristics to report on a DT case study called the Flex-cell, a manufacturing cell with two robotic arms used for cooperative assembly. The generalizability of this unified characterization is validated using a multi-case approach with another case study in robotics and another in the food industry. We call on the DT community to integrate these systematic reporting principles in their future DT experience reports such that other practitioners can learn from each other more effectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.348 | 0.360 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".