Tool wear morphology and life under various lubrication modes in turning stainless steel 316L
Bibliographic record
Abstract
One of the most important industrial demands is to improve the machinability of hard steels. Among hard steels, 316L stainless steel has significant mechanical and thermal properties recommended for many industrial applications and products, including aero-engines and gas turbines. Similar to many other hard-to-cut materials, the machining of 316L stainless steel requires an adequate selection of cutting parameters and lubrication modes. Limited studies have been found in this regard. In the present study, turning tests were conducted on 316L steel and the tool wear magnitude and morphology were assessed under four lubrication modes. Furthermore, to improve machining performance, a minimum quantity lubrication (MQL) system was used and the obtained results under this lubrication mode were compared to three other types of lubrication modes, including dry, wet, and MQCL. The composition of the adhered material on the cutting tools shows that in all lubrication modes, built-up edge (BUE) occurred. Furthermore, the use of high pressure-lubricated machining (MQCL) led to longer tool life as compared to the other three lubrication modes used. In fact, double the tool life was recorded for the cutting tools used under the MQCL mode.
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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 | 0.001 | 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".