Thermal field in automated fiber placement of thermoplastic composites: novel experimental contact sensing and conjugate multi-physics numerical modeling
Bibliographic record
Abstract
The thermal field around the deposition region during Automated Fiber Placement (AFP) of thermoplastic composites (TPCs) critically governs the quality of the final part. In this work, a new experimental technique is introduced to capture the internal temperature of the incoming tape throughout its entire trajectory, covering regions of the tape before, at, and after the nip point. Engineered sensor tapes are fabricated to replicate the geometry and properties of the actual composite tape, with an embedded fast-response fine thermocouple, allowing direct feeding of the sensor tape into the AFP head during operation. This method enables direct temperature measurements within critical regions previously inaccessible to infrared thermography and impractical for conventional thermocouple placement. Subsequently, a high-fidelity three-dimensional conjugate heat transfer model is developed using the finite volume method to simulate the thermal field during hot gas torch (HGT)-assisted AFP. After validation against the experimental data, a computationally efficient data-driven surrogate, based on multivariate third-order polynomial regression, is trained on simulation results to yield closed-form predictive equations for rapid calculation of critical thermal responses (e.g., nip point temperature, maximum temperature, and immediate cooling rate) from primary input process parameters.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".