Determination of convective heat transfer coefficient for automated fiber placement (AFP) for thermoplastic composites using hot gas torch
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Bibliographic record
Abstract
In heat transfer analysis of AFP process using a hot gas torch, the convective heat transfer which occurs between the hot gas flow generated by a torch nozzle and a composite substrate plays an important role in the heat transfer mechanism. In order to model the convective heat transfer, a local heat flux equation is utilized where is the energy flow per unit of area per unit of time, h is the convective heat transfer coefficient between the hot gas torch and the composite surface, and accounts for the temperature difference between the two media. This coefficient h is dependent on various number of parameters such as nozzle geometry and its configuration relative to the surface of the substrate, type and configuration of the roller, gas flow rate, temperature of the gas, type of the gas etc. Researchers on the heat transfer analysis for automated composites manufacturing have used values of h that vary from 80 W/m2K to 2500 W/m2K. This large range gives rise to uncertainties in the determination of important behavior such as the temperature distributions, residual stresses, and deformations of the composite structures due to the manufacturing process. The reason for these large differences can be due to the differences in the process parameters in each of the studies. The process parameters can include the volume flow rate of the hot gas, the gas temperature, the distance between the nozzle exit and the surface of the composite plate, the angle of the torch with respect to the surface of the substrate etc. In addition, the value of the h coefficient may not be constant over the heating length of the process. The purpose of this paper is three fold: 1. To investigate the AFP process parameters that may affect h. 2. To investigate different methods for the determination of h, and 3. To develop a procedure for less-time-consuming determination of h for the purpose of analysis for residual stresses and deformations.
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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) | 0.000 | 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