Surface engineering strategies for aerospace composite repairs: Machining and texturation of additive manufacturing parts by abrasive waterjet
Bibliographic record
Abstract
In aerospace maintenance, repair and overhaul operations, additive manufacturing (AM) holds great potential for composite repair, providing a precise and customized approach to fabricating repair patches for damaged structures and reduce waste. Given the circularity and economic advantages, repairing composite structures is often preferable to replacement. This study explores the use of abrasive waterjet process for machining and texturing AM composites composed of micro carbon infused nylon matrix and continuous carbon fiber reinforcement . Four surface conditions were investigated: (I) machined without additional surface preparation, and (II, III, IV) machined followed by three levels of texturation - good, medium, and poor. These surfaces are quantitatively evaluated based on crater volume (Cv) and arithmetic mean height (Sa). The mechanism of material removal was investigated by surface texture analysis and scanning electron microscopy. A prediction model was developed and experimentally validated for assessing the correlation of Cv and Sa. Results show an ascending trend in both Cv and Sa values from condition I to IV. The study reveals important findings on machined surface characteristics and their preparation for adhesive bonding , which are crucial for integrating repair patches onto parent structures.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".