Digital twin-based design and simulation frameworks for manufacturing engineering enabling virtual prototyping and lifecycle optimization
Bibliographic record
Abstract
Manufacturing engineering is undergoing a fundamental shift as increasing product complexity, shorter development cycles, and cost pressures expose the limitations of physical prototyping and siloed design-operation workflows. From a broad perspective, digital twin technologies have emerged as a foundational enabler for data-driven engineering, providing virtual representations of physical assets, processes, and systems that evolve continuously over their lifecycle. By coupling physics-based models with real-time operational data, digital twins offer a means to unify design, simulation, validation, and optimization within a single coherent framework. This paper develops a digital twin-based design and simulation framework for manufacturing engineering that enables virtual prototyping and lifecycle optimization. The framework integrates high-fidelity simulation models, sensor-driven data assimilation, and control-oriented analytics to support design exploration, performance prediction, and operational decision-making before physical realization. Virtual prototypes are used to evaluate design alternatives, assess manufacturability, and quantify trade-offs across cost, quality, energy consumption, and reliability under realistic operating conditions. Narrowing to lifecycle application, the proposed framework extends beyond the design phase to incorporate commissioning, production, and maintenance stages. Real-time data from manufacturing systems continuously update the digital twin, allowing deviations between expected and actual performance to be detected early and corrective actions to be simulated virtually before deployment. This closed-loop interaction supports predictive maintenance, process reconfiguration, and gradual design refinement based on in-service behavior rather than static assumptions. Simulation results and illustrative industrial use cases demonstrate that digital twin-enabled virtual prototyping reduces development time, lowers physical testing costs, and improves lifecycle performance compared to conventional design approaches. By embedding simulation, data, and optimization across the full manufacturing lifecycle, the framework positions digital twins as a practical engineering instrument for achieving resilient, efficient, and adaptive manufacturing systems.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".