Experimental and Numerical Study of Different Methods' Effects on Lubricant Flow on Temperatures and Strains of Turning Cutting Tool (HSS)
Bibliographic record
Abstract
The aim of the present study is to facilitate the machining process of cast steel (HSS) through cooling and lubricating by compressed air with liquid in three directions and consisting of (oil, soap, carbolic acid, and sculpture) and was compared with the lubricant process in one direction and at different cutting speeds (2.08, 6.25, 10.4, and 14.16 m/sec). The Auto Desk Inventor programme is used to simulate the cutting process by applying cutting forces to the tool's shear surface. The interface equipment is used to measure cutting tool strains by the response of a strain gauge and the Arduino equipment to measure cutting tool temperature. In addition, the tool strain equations were used assuming that the cutting tool is fixed between the two walls (fixing region and surface of the workpiece) to get the best results at the same speed. The results refer to the lubricant in three directions, which is better than one direction due to decreasing cutting tool strain, reaction of cutting force on the shear surface of the cutting tool, and cutting tool temperature. The experimental results show that a cutting speed of 10.4 m/sec is the best for the cutting process. Furthermore, the numerical results are converged with practical results in a small correction factor (0.428), preventing the tool from vanishing.
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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 | 0.000 | 0.000 |
| 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 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".