EXPERIMENTAL EVALUATION OF PARAMETERS AFFECTING DELAMINATION FACTOR, TENSILE STRENGTH, THRUST FORCE AND SURFACE ROUGHNESS IN DRILLING OF GFRP
Bibliographic record
Abstract
The drilling of glass fiber-reinforced plastic (GFRP) composites gained importance since they are used as structural components in many industries such as automotive, aerospace, and aviation. A large number of holes are needed in the industry to join these composite parts. However, some failures occur in drilling GFRP composites, such as delamination, matrix cracking, and fiber breakage. These failures not only reduce the strength of the composite, but also reduce its service life. Drilling parameters, drill bits, and woven types have a great influence on the occurrence of these failures by greatly influencing the thrust force, surface quality, and cutting temperature. In this study, the effects of drilling parameters and woven types of GFRP composites on thrust force, surface roughness, delamination factor, and cutting temperature were examined in the drilling of uni-directional (UD), [Formula: see text] and 0[Formula: see text] GFRP woven composites. The effects of drilling parameters and the delamination factor on the tensile strength of the drilled specimen were also investigated. The result of this study indicated that thrust force, delamination factor, and surface roughness increased with increasing cutting speed and feed rate. An increase in feed rate decreased the cutting temperature, while an increase in cutting speed increased the cutting temperature. Also, it was found that the delamination factor had a critical influence on the tensile strength of the GFRP composites.
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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 | 0.001 |
| 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".