Environmental Assessment and Optimization When Machining with Micro-textured Cutting Tools
Bibliographic record
Abstract
Abstract The dry machining strategy has recently received high attention in the field of metal cutting as it can eliminate the environmental impacts associated with the usage of cutting fluids. However, high-generated heat and severe tool wear are usually observed for the dry machining operations. One of the suggested techniques to improve the dry machining performance is to utilize the textured cutting tools, reducing the friction at the chip-tool interface. In this study, three different micro-textured tool designs were used during the machining AISI 1045 at different cutting conditions. A life cycle assessment was performed including the power consumption for preparing the textured tool designs and the measured power during the machining experiments. Furthermore, some measured machining outputs (flank wear, surface roughness, and the unit volume machining time) were further included to offer a comprehensive and effective sustainability assessment for the performance of the utilized textured tools. The performance of these textured tools was also compared with the non-textured tool under the same cutting conditions. The textured tool design with narrow micro-groove width showed better sustainable performance compared to the non-textured tool and other textured tool designs.
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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".