In-situ consolidation of thermoplastic composites by automated fiber placement: Characterization of defects
Bibliographic record
Abstract
The emergence of automated manufacturing of composites has not only transformed the manufacturing of optimized and geometrically complex structures but has also expanded the promising prospect of in-situ manufacturing of thermoplastic composites (TPC), where both material placement and consolidation are carried out by automated fiber placement (AFP) equipment, streamlining the process into single step manufacturing. However, the inherent complexities in different aspects of robotic automation, imperfections in the supplied material, and the occurrence of multi-physical phenomena during in-situ consolidation introduce various manufacturing-induced defects. While the defects in thermoset composites (TSC) made by AFP have been widely studied in the past, this study explores the diverse defects at micro and macro scales for TPCs made by AFP, with a focus on carbon-fiber/poly-ether-ether-ketone (CF/PEEK) tapes consolidated using hot gas torch (HGT) heating system. An overview of defects and associated characteristics is presented across three phases: defects in supplied impregnated tapes, defects and limitations in performance of AFP system, and defects in the final in-situ consolidated composite. For the defects subject to studies in the past, the description is limited to a concise review, while those with limited understanding are supported by new empirical observations in this work.
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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 | 0.001 | 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".