Analysis of Stresses in CFRP Composite Plates Drilled with Conventional and Abrasive Water Jet Machining
Bibliographic record
Abstract
The aim of this paper is to analyze the influence of two machining processes on the mechanical behaviour of composite plates under cyclic loading. For this purpose, an experimental study using several CFRP plates drilled with conventional machining and non-conventional machining (abrasive water jet) was carried out. Digital image correlation and static tests using an Instron 4206 tester were performed. In addition, infrared thermography (IR) and fatigue tests were also performed to assess temperature and damage evolutions and also the stiffness degradation. Fatigue results have shown that the damage accumulation in specimens drilled with conventional machining was higher than the abrasive water jet ones. Furthermore, the endurance limit for plates drilled conventionally was approximately 10% higher than those drilled with abrasive water jet. This difference was related to the initial surface integrity after machining induced by the difference in the mechanism of material's removal between the two processes. The difference in surface texture was responsible for the initiation of stress concentration sites as evident from IR camera’s stress analysis. This was confirmed by SEM tests conducted after a destructive sectioning of the specimens before fatigue testing.
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 | 0.000 |
| 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".