Building Discrete-Event Simulation for Digital Twin Applications in Production Systems
Bibliographic record
Abstract
Digital equivalence is the main objective of Digital Twin (DT)s, and simulation is an integral part. DTs reach beyond traditional simulation with the help of real-time synchronization through Industrial Internet of Things (IIoT) technologies. Simulation supports off-line experimentations and planning, while DTs offer synchronous execution and modification. DTs help to understand “what may happen”. Also, they present “what is happening” and its management methodologies. In this paper, we present building aspects and an integration approach for a Digital Twin based Discrete-Event Simulation model. Our approach utilizes a data-driven agent-based simulation within a DT framework. It presents an integration layer that provides two essential features: reconfiguration and state initialization. It gives simulation models configurability and integrity that are required for operating within a DT. Our proposed approach is presented through a use case of a semiconductor manufacturing system. The proposed integration layer extends the usability of current Discrete-Event Simulation (DES) for a DT within a Cyber-Physical Production System.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".