The effect of automated fiber placement process parameters on interlaminar shear strength of uncured prepreg bonded samples
Bibliographic record
Abstract
The effects of automated fiber placement (AFP) parameters on the inter-laminar bonding between the uncured thermoset prepreg tapes were investigated using a systematic series of experiments and FE analysis. The goal was to optimize inter-laminar bonding during the AFP lay-up process and provide a model for the interlayer bonding of uncured prepreg tapes during this process. The shear strength of the interfacial bonding plays a pivotal role in the formation of planar and non-planar deformations during the automated fiber placement (AFP) process. The quality of this bonding has a significant effect on the quality of the manufactured parts. Besides, the bonding strength is interconnected with various AFP process parameters including compaction roller, feed rate, temperature, and dwell time. Hence, a systematic series of experimental studies are conducted to investigate how changes in process parameters affect the shear strength of single lap joint (SLJ) specimens produced under various process conditions. To fabricate the single-lap joint samples under different conditions, an in-house setup was developed to simulate the AFP process allowing us to control compaction force, feed rate, temperature, and dwell time during the process. The experimental results of the single lap joints indicate that the shear strength of the bonded prepreg tows is significantly influenced by the interaction among the process parameters rather than by their individual, isolated effects. Moreover, the responses of prepreg SLJs are simulated using the FE method. Through the comparison of numerical and experimental results, it will be clearly shown that the developed FE framework can act as a reliable approach for modeling the bonding layer between prepreg tapes.
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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) | 0.001 | 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".