Role of conventional drilling and abrasive water jet induced damage on the mechanical response of flax/epoxy laminates with holes
Bibliographic record
Abstract
Many engineering applications using composite materials require an assembly of components which can be accomplished using adhesives, welding, or fasteners. The last requires the machining of holes, which are usually done via conventional drilling or abrasive water jet process. The choice of the machining process is important because it has a direct impact on the surface quality of the machined holes, and, consequently, on the performance of the composite structure. Although most studies on the effect of machining methods were focused on conventional composites, little is known about the effect of conventional drilling (CD) and Abrasive water jet (AWJ) machining methods on the mechanical behaviour of flax composites with holes. This study is the first to experimentally investigate the influence of the machining process in pure flax/epoxy composites with three different layups, namely, unidirectional (F[0]), cross-ply (F[0/90]) and angle-ply (F[±45]). A delamination factor was employed to evaluate the degree of delamination damage around the surface of the hole. The lowest delamination factor at the exit of the hole was observed for F[0]-AWJ with a value of 0.777 and the highest was observed for the F[±45]-AWJ with a value of 1.484. Damage analyses of the layups under a quasi-static loading condition showed that the average final stiffness damage for F[0]-CD was 13% greater than that of F[0]-AWJ, and for F[0/90]-AWJ and F[±45]-AWJ were respectively 25% and 27% greater than their counterpart samples machined by CD method. The damage results correlate well with the delamination factor.
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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) | 0.001 | 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".