Influence of the texturing quality consecutive to Abrasive Water Jet machining on the adhesive properties in mode I of 3D woven composite assemblies
Bibliographic record
Abstract
The aim of this study is to analyse the suitability of Abrasive Water Jet Machining (AWJM) as a surface preparation technique for adhesive bonding of 3D woven CFRP substrates and to investigate the influence of the texture-induced quality of the adherends on the adhesive properties of the bonded joints. For this, three specimens with varying levels of texturing qualities were produced by AWJM and quantified using classical criteria such as average roughness “Ra and Sa” as well as a new criterion named “crater volume” (Cv). After adhesive bonding, the assemblies with different levels of texturing quality were subjected to Double Cantilever Beam tests to obtain their critical energy release rate in mode I (GIc), which was compared to values of bonded assemblies prepared by sanding as performed in the industrial context. For the analysis of the damage mechanisms, DCB tests were multi-instrumented with different techniques such as X-ray tomography and digital image correlation. The obtained results showed that, the evolution of GIc as a function of the textured surface quality presented a good correlation with Cv compared to the classical parameter Ra or Sa. In addition, assemblies prepared by AWJM present higher values of GIc compared to sanded ones (up to +60%), with a downward parabolic relationship between GIc and Cv. Post-mortem X-Ray tomography analysis has revealed a change in the crack propagation scenario as Cv increases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".