An Overarching Quality Evaluation Framework for Additive Manufacturing Digital Twin
Bibliographic record
Abstract
The key differentiation of digital twins from existing models-based engineering approaches lies in the continuous synchronization between the physical and digital twins through data exchange. The success of digital twins, whether operated automatically or with humans in the loop, hinges on the quality of data, models, and computations, which influences the digital twins’ usability and effectiveness. This paper provides a framework for tracking digital twin performance in Additive Manufacturing (AM) applications and developing quality assessment tools to enhance the development and applications of AM digital twins. This framework is founded on a digital twin development activity model, which identifies the digital objects through the digital twin evolution life cycle, delineating their quality measures and the transfer of any quality-related problems. Quality metrics are defined for each type of digital objects in the framework and computation methods are outlined to track how quality issues in earlier stages affect subsequent activities and digital objects. The data characteristics linked to digital twin effectiveness, as identified by the framework, could serve as key performance indicators for AM data management. Furthermore, understanding the challenges in the uncertainty and quality transition between digital objects can lead to a strategic research agenda for AM digital twins.
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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 | 0.024 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".