Optimization of surface texturing parameters in additively manufactured continuous fiber composites using abrasive waterjet technique for composite repair applications
Bibliographic record
Abstract
• Abrasive waterjet and additive manufacturing provide novel solutions for texturation. • Waterjet pressure and traverse speed are key factors affecting surface texturation. • Cv and Sa are more reliable surface profile metrics than Ra, Rv, and Rz. • Texturation prediction models for Cv and Sa closely align with experimental results. • Microscopy reveals material removal mechanisms and abrasive particle embedment. Surface texturing is critical in adhesive bonding strength, hence crucial for composite repair. This study evaluates the influence of abrasive waterjet (AWJ) machining as a texturing technique for surface preparation of additively manufactured (AM) composite parts. Effects of waterjet pressure (WP) and traverse speed (TS) on crater volume (Cv) and arithmetic mean height (Sa) were studied. Digital and electron microscopy validated surface textures, observed damage patterns, and assessed contamination due to abrasive embedment. Increasing WP from 60 to 100 MPa significantly increased Cv by 179.6 % and Sa by 410.6 %, highlighting its strong influence. In contrast, TS showed a secondary effect when increased from 10 to 20 m/min, with higher speeds producing smoother surfaces and reducing Cv and Sa by 30.78 % and 28.59 % respectively. The findings, supported by statistical analysis and multi-objective optimization, show that Cv and Sa are effective metrics for surface texture quantification in AWJ textured AM composite specimens.
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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 | 0.000 |
| 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 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".