Response Sensitivity and Parameter Importance in Composites Manufacturing
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Bibliographic record
Abstract
In this article we derive, implement, and verify equations to compute the sensitivity of responses from numerical simulation of composites manufacturing. The responses considered are part temperature and degree of cure, as well as process-induced deformation of the cured part. The `direct differentiation method' (DDM) is used, which entails a one-time investment of effort to differentiate the governing response equations analytically. The implementation of the derivative equations facilitates efficient and accurate computation of response sensitivities in all subsequent analyses. This article extends the DDM methodology developed earlier for mechanical problems. Novel `shape sensitivity' equations and efficient implementation techniques are also included. In order to verify the implementations, the model predictions are compared with those obtained from the less efficient finite difference approach. A comprehensive example is presented where the usefulness and interpretation of response sensitivities are emphasized. It is observed that the responses are particularly sensitive to certain model parameters, for which further data gathering and model improvement efforts should be focused.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it