Probability-Based Modelling of Composites Manufacturing and Its Application to Optimal Process Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The control of process-induced deformations in composite structures is important for cost-effective manufacturing. In recent years, significant advances have been made in predicting the average deformation behaviour, but little work has been done in predicting the variability, which results from uncertainties in both the raw material properties and the manufacturing process conditions. A probability-based approach is presented in this paper for predicting the variability of process-induced deformations. A two-dimensional finite element code, which deterministically simulates the various physical phenomena during processing of composite structures, is integrated with a first-order reliability analysis method to calculate the probability of the deformations exceeding a specified allowable tolerance. The methodology is demonstrated through two case studies. In the first study, a probabilistic description of the process-induced spring-in of a channel section is achieved and the effect of variability in material properties on the final channel angle is studied. In the second study, the optimal tool-shape for the channel section is determined by coupling reliability analysis with a simple cost model of the manufacturing process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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