Comparative Study of the Effect of Dry, Mineral Oil, and TiO2 Nano-Lubricant on Tool Wear During Face-Milling Machining of Ti-6al-4v-Eli Using Carbide Tool Insert
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Bibliographic record
Abstract
Titanium alloys are valuable materials in the manufacturing industry. They are applied to develop major parts in the aerospace, automobile, and aeronautic industry. The major challenge faced by the manufacturer is the ability to cut Titanium alloys to the specific shape desired. The influence of nanoparticles as an additive to the mineral oil is vital in the machining of Titanium alloys to reduce vibration and friction, leading to high wear in the cutting tool. The quest for a sustainable and reliable method to minimize tool wear and increase efficiency has led to the nano-lubricants application during metal machining. Therefore, this research focus on the impact of three machining cutting conditions on TI-6AL-4V-ELI during face-milling operations. The Taguchi experimental design method was employed to study the machining factors effects on the tool wear by varies the machining factors, such as Cutting speed: 2000 rpm, 2500 rpm, and 3000 rpm, Feed rate: 150 mm/min, 200 mm/min, 250 mm/min, Depth of Cut: 0.3 mm, 0.6 mm, and 0.9 mm and the cutting conditions: dry, mineral oil and Titanium Oxide (TiO2) nano-lubricant to study their effects on the Carbide Insert cutting tool wear minimization for improving workpiece's machinability. This research also studies the interactions of the machining factors on the tool wear under the three lubrication cutting conditions. The findings confirm that the state of TiO2 nano-lubricants decreased the tool wear for all the experimental runs. The signal-to-noise ratio results from the Taguchi design show that the cutting condition has a significant role in having 5.794. The cutting speed of 2.145, the feed rate of 0.789, and the depth of cut were 0.724. Furthermore, the generated model could predict the cutting tool wear rate with 97%, proving that the experimental result is viable for application in the manufacturing industry.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it