Self-lubrication of machining tools in dry via in-situ formed tribofilms.
Bibliographic record
Abstract
Green manufacturing technology has become a significant innovative keyword to reduce the environmental burden and CO2 emission at the stage of manufacturing and production. In daily operations by machining, huge amount of lubricating oils and cleansing agents is wasted so that dry machining technology provides us a way to completely reduce these wastes. Among several candidates, self-lubrication via in-situ formation of lubricious oxide films is accommodated to protective coatings in order to attain low friction and wear state even at higher cutting speed range. Materials science of in-situ formed lubricious oxide tribofilms is stated with consideration of accommodation mechanism via the chlorine implantation. Mechanical characterization is made for evaluation on elasto-plastic deformation of lubricious oxides. Turning test is employed to evaluate dry machinability of various tools for wide range of cutting speed. Self-lubrication in dry machining is described both for bare WC and TiCN-coated WC tools with and without chlorine implantation. Precise microstructure analyses are made by using the laser microscope, EDS and XPS. In-situ formation of lubricous oxides proves that self-lubrication process takes place even in dry machining to reduce the flank wear and friction coefficient.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".