Internal cooling system effects to heat transfer and tool wear in turning operations of titanium alloys Ti6Al4V
Bibliographic record
Abstract
Internal cooling systems are essential for turning operations to improve the efficiency and quality of the machining process. In this paper, application of the virtual machining system is developed to analyze the effects of internal cooling systems on heat transfer and tool wear in turning operations of titanium alloys Ti6Al4V. The cutting forces as well as cutting temperature along machining paths are simulated in the virtual machining system to predict and analyze the effects of internal cooling system to the heat transfer and tool wear during turning operations. The coolant flow inside the internal cooling channels of a rotating cutting tool is simulated using computational fluid dynamics. Next, utilizing the modified Johnson–Cook model, the cutting temperature during turning operations is determined. The finite element method is employed to predict tool wear by applying the Takeyama–Murata analytical method. To validate the study, experimental works are implemented using turning machine tool. The cutting settings during experimental works are feed rate of 0.3 mm/rev, depth of cut of 2 mm, and cutting speed of 75 m/min. Thus, using the proposed virtual machining system, the performance of internal cooling system can be enhanced to increase cutting tool life and surface quality of machined parts in turning operations.
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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 | 0.000 | 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 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".