Surface roughness and energy consumption analysis of conventional and peck drilling approaches
Bibliographic record
Abstract
Drilling operations are one of the most commonly used operations in the automotive and aerospace sectors. The aim of this article is to compare peck drilling as an alternate approach to the conventional drilling and reaming operations; in terms of energy consumption and machined surface roughness to facilitate the selection of the optimum finishing processes with respect to machined surface quality and energy consumption. The experiments were performed under dry conditions on an Al-6061 using a high-speed steel reamer and drills of 12 mm diameter. The results revealed that peck drilling refined the surface finish of previously drilled steps in most of the cases. The outcome of the energy consumption analysis was used to evaluate the amount of CO 2 emissions. The study suggested that surface roughness refinement in peck drilling was better than conventional drilling but was not as efficient as the reaming process. Peck drilling generated surfaces with a roughness value between those of drilling and reaming operations. Less tool wear was observed under peck drilling process when compared with conventional drilling. The investigation also revealed that CO 2 emissions produced under peck drilling approach were slightly higher than for combined drilling and reaming approach.
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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 | 0.001 | 0.001 |
| 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".