A Digital Image Analysis to Evaluate Delamination Factor after Drilling GFRP Composites using a Kevlar Drill Bit
Bibliographic record
Abstract
Objectives: Evaluation of three important delamination factor models and compare their values with varying spindle speeds and feed rates in drilling GFRP composites. Methods: Digital image analysis is adopted for measurement of the dimensions of delamination damage. Accurate assessment of delamination damage is essential for the analysis and design of optimum drilling parameters. Experiments were conducted on GFRP composite materials with feed rates ranging from 100-400 mm/min and spindle speeds ranging from 1000-2500 rpm. Findings: These experiments reveal that delamination reduces with increase in the spindle speed and reduction in the feed rate, but higher spindle speeds may increase the delamination damage. Results also reveal the consideration of area of delamination damage in the assessment of delamination is more important than the maximum damage diameter. Applications: The good mechanical properties of GFRP composites allow their use in compartment panels and doors. Digital image analysis improves the accuracy in measurement of delamination damage.Keywords: Delamination, Digital Image Analysis, Feed Rate, GFRP, Kevlar Drill Bit, Spindle Speed
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".