A Novel Technique to Achieve Sustainable Machining System
Bibliographic record
Abstract
Turning is one of the most fundamental and indispensable processes of metal removal in industry. Increasing pollution-preventing initiatives globally and consumer focus on environmentally conscious products has put increased pressure on industries to minimize or eliminate the use of cutting fluids. The use of solid lubricant in machining operation is one of the most effective strategies in this direction to achieve sustainable machining system. In the present research work, the feasibility of a novel approach for developing a new generation of machining technique namely High Pressure Minimum Quantity Solid Lubricant experimental set-up has been envisaged with an aim to improve process performance and to eliminate the use of cutting fluids in machining operation. A detailed comparison has been made with wet, dry, MQL machining operation to assess the process performance on the basis of tool wear and surface finish. The results indicate that HP-MQSL mixture at a small and constant flow allows better penetration of the mixture into the tool-work and tool-chip interface, thus providing reduction on the tool wear and surface roughness more effectively than a wet, dry, and MQL machining at high speed conditions, thereby to achieve sustainable machining system.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".