Influence of Water-Miscible Cutting Fluids on Tool Wear Behavior of Different Coated HSS Tools in Hobbing
Bibliographic record
Abstract
The present paper describes the influence of water-miscible cutting fluids on tool life (flank wear) and crater wear of various coated cutting tools and finished surface roughness, as compared with the cases of dry cutting and wet cutting using cutting oil in hobbing in an attempt to improve the working environment. Experiments were conducted by simulating hobbing by fly tool cutting on a milling machine. The following results were obtained. (1) In the case of an uncoated tool, cutting oil was more effective than dry cutting in reducing flank wear. Cutting oil and water-miscible cutting fluids were more effective in reducing flank wear than dry cutting using TiN- and TiAlN-coated tools. The use of water-miscible cutting fluids in conjunction with TiSiN- and AlCrSiN-coated tools prolongs tool life. (2) For all coated tools, the use of cutting oil or water-miscible cutting fluids were effective in reducing crater wear. Especially, water-miscible cutting fluids were effective for TiSiN- and AlCrSiN-coated tools. (3) Regarding the finished surface roughness, in the case of dry cutting, the finished surface roughness was similar for various types of coating films. When using cutting oil or a water-miscible cutting fluid, the finished surface roughness improved compared with dry cutting, independent of the type of coating film applied. The finished surface roughness obtained using water-miscible cutting fluid was approximately the same as or smaller than that obtained using cutting oil. (4) With respect to flank wear, crater wear, and finished surface roughness, the water-miscible cutting fluid of emulsion type containing a large amount of synthetic lubricating additives was suitable for the AlCrSiN-coated tool.
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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) | 0.001 | 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".