Determination of convective heat transfer coefficient for hot gas torch (HGT)-assisted automated fiber placement (AFP) for thermoplastic composites
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Bibliographic record
Abstract
In-situ manufacturing of thermoplastic composites using the automated fiber placement (AFP) process consists of heating, consolidation and solidification steps. During the heating step using hot gas torch (HGT) as a moving heat source, the incoming tape and the substrate are heated up to a temperature above the melting point of the thermoplastic matrix. The convective heat transfer occurs between the hot gas flow and the composites in which the convective heat transfer coefficient h plays an important role in the heat transfer mechanism which in turn significantly affects temperature distribution along the length, width and through the thickness of the deposited layers. Temperature is the most important process parameter in AFP in-situ consolidation that affects bonding quality, crystallization and consolidation. Although it is well known the convective heat transfer coefficient h is not constant and has a distribution, most studies have assumed a constant value for h for heat transfer analysis which leads to discrepancy between numerical and experimental results. In this study a new function is proposed to approximate the distribution of the convective heat transfer coefficient h in the vicinity of the nip point. Using the proposed convective heat transfer coefficient distribution, a three-dimensional finite element transient heat transfer analysis is performed to predict temperature distribution in the composite parts. An optimization loop is employed to find the free parameters of the distribution function so that the predicted temperature match experimental data. It is shown that, unlike other studies assuming constant h value, not only maximum temperature can be well predicted, but also predicted heating and cooling curves agree well with experimental results. The cooling rate is of significant importance in crystallization behavior and residual stress calculation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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