Investigating Drilling Efficiency: A Study on Indexable Centerless Drilling of Ti-6Al-4V Alloy
Bibliographic record
Abstract
Abstract Titanium Alloy (Ti-6Al-4V), is highly regarded in the aerospace industry due to its exceptional strength-to-weight ratio. The alloy's low thermal conductivity and high tensile strength pose machining challenges, leading to increased tool temperatures and mechanical stress. The conventional use of solid carbide drills is hindered by substantial tool wear. To improve tool life, prior research has delved into various cutting strategies, ranging from flood cooling to minimum quantity lubrication (MQL), enduring challenges persist. This study introduces an innovative approach, leveraging Titanium Aluminum Nitride (TiAlN) coated indexable centerless inserts to bore holes in Ti-6Al-4V under three distinct cutting conditions: dry, flood cooling, and MQL. These conditions are scrutinized across varied feed rates (60 mm/min, 100 mm/min, and 120 mm/min) with a fixed spindle speed of 1200 rpm. The study's primary focus is on key output parameters, including surface roughness (SR), tool life, and cutting temperature. From the parametric and surface topographic analysis, the findings reveal that under the flood cutting approach with a 60 mm/min feed rate, the indexable inserts excelled when drilling Ti-6Al-4V. This combination delivered a better surface quality (Ra = 1.66 µm), extended tool life (27814.27 mm 3 material removed and 18 holes drilled), and lower cutting temperature (881°F). Additionally, scanning electron microscopy (SEM) analysis corroborates that most common types of wear observed were abrasion, delamination, cracking, and edge fracture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".