Ranking of novel criteria for prioritising production-relevant quality-critical product parameters of electrolysers and fuel cells
Bibliographic record
Abstract
One promising approach to accelerate the adoption of fuel cells and electrolysers is the use of Digital Concepts (DCs), such as Digital Twins (DTs) which simulate with quality-critical product parameters in manufacturing. Despite potential benefits, the application of mature DCs for optimising processes and products remains limited. One key reason for this is the unfamiliarity regarding the connection between quality-critical parameters and DCs. To address this gap, a multi-criteria decision-making framework is proposed, which employs novel criteria conceived for prioritising quality-critical parameters for simulation applications, referred to as Simulation Decision Matrix. Criteria are weighted using the analytical hierarchy process in expert interviews, based on a novel use case. The interviews revealed that a profound understanding of differential equations, formulas, and physical parameters of the system to be modelled is essential for developing a DC. Additionally, knowledge of model assumptions and simulation limits is crucial for the proper application of a DC. The integration of quality-critical parameters into the model requires finesse and was highly rated by the experts. Furthermore, validation is paramount, as the robustness of the DC and its seamless integration into the production environment are key for their successful application. The experts showed sensitivity to the use case, ranking the relevant criteria accordingly. Their feedback underscores both the relevance and complexity of this topic, positioning this work as a valuable starting point for further exploration. • Quality assurance in the production of electrolysers and fuel cells is examined. • A new simulation-based manufacturing optimisation method is presented as a use case. • A decision matrix for prioritising quality-critical product parameters is developed. • Criteria are weighted using the analytical hierarchy process in an expert survey. • Validation is the most relevant criterion for simulation applications in production.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| 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".