A Digital Twin for Integrated Inspection System in Digital Manufacturing
Bibliographic record
Abstract
Coordinate metrology is a crucial part in advanced manufacturing industries to achieve and maintain conformance of high-quality products within design specifications. Meanwhile, software-components are increasingly becoming an essential part of the inspection process because of increasing part complexities in design and the high-volume of data captured from different sensors in hardware-components. This paper presents a virtual replica to work parallel to an integrated inspection system (IIS) for inspection of freeform and complex surfaces based on a metric of their geometric complexity. In this approach, an intelligently guided sampling is virtually conducted from a large dataset, instead of the physical sampling process when the sample points are traditionally selected randomly from the measured surface. Implementation of a closed-loop between the main tasks in IIS is considered in developing this digital twin to reduce the uncertainties associated with the inspection process. A method is introduced to estimate the local densities of the measured points required for virtual sampling from each patch on the work-pieces’s surface based on its geometric complexity. Two case studies are conducted to verify the effectiveness of the methodology. The observed efficiency in selection of the important measured data in the proposed sampling strategy makes it a better sampling strategy to be implemented in a digital twin for IISs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".