Heat transfer analysis of automated fiber placement of thermoplastic composites using a hot gas torch
Bibliographic record
Abstract
With more and more use of composites in engineering applications, the need for automated composites manufacturing is evident. The use of automated fiber placement (AFP) machine for the manufacturing of thermoplastic composites is under rapid development. In this technique, a moving heat source (hot gas torch, laser, or heat lamp) is melting the thermoplastic composite tape and consolidation occurs in situ. Due to the rapid heating and cooling of the material, there are many issues to be addressed. First is the development of the temperature distribution in different directions which gives rise to temperature gradients. Second is the quality of the bond between different layers, and third is the rate of material deposition to satisfy industrial demand. This paper addresses the first issue. The temperature distribution affects the variation in crystallinity, and residual stresses throughout the structure as it is being built. The result is the distortion of the composite laminate even during the process. In order to address this problem, first the temperature distribution due to a moving heat source needs to be determined. From the temperature distribution, the development and distribution of crystallinity, residual stresses and deformation of the structure can then be determined. As the first phase of the work, this paper investigates the temperature distribution due to a moving heat source for thermoplastic composites, without considering the material deposition. A finite difference (FD) code based on energy balance approach is developed to predict the temperature distribution during the process. Unidirectional composite strips are manufactured using AFP and fast-response K-type thermocouples (response time of 0.08 s, as compared to normal thermocouples with response time of 0.5 s) are used to determine the thermal profiles in various locations through the thickness of the composite laminate subjected to a moving heat source. It is shown that temperature variations measured experimentally during the heating pass, using thermocouples embedded into the composite substrate, underneath layers of the composite material, are consistent with the generated thermal profiles from the numerical model. The temperature distribution, in both the direction of the tape and through-thickness direction can be predicted numerically.
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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 | 0.000 | 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) | 0.001 | 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".