INFLUENCE OF THE USE OF CARAPA GUIANENSIS OIL (ANDIROBA OIL) COMPARED WITH COMMERCIAL CUTTING FLUID IN THE TURNING PROCESS OF THE ABNT 1045 STEEL GRADES
Bibliographic record
Abstract
The increasing technological advances obtained both in the development of new materials and of machine tools increased the demand for the machining processes and in addition, the use of increased cutting fluids. However, it’s necessary to have characteristics that don’t harm the environment and the operator. In machining processes, cutting fluids, when properly chosen and applied, may reflect benefits during the manufacturing process. This work evaluated the performance of a commercial cutting fluid by comparing it with vegetable oil extracted from carapa guianensis in the abnt 1045 steel turning process. The cutting speed (vc), tool feed (f) and depth (ap) and the influence of the use of both of them on the metal was verified with the following variables: chip analysis, surface finish, cutting temperature and tool wear. It was observed that with the use of andiroba oil, better chip was generated for the safety of the operator, higher cutting temperatures in the piece, higher tool wear and better surface finish with a difference of 23% compared to commercial cutting fluid. Thus, the fluid from andiroba based on the conventional application demonstrated a viable alternative in the turning process of abnt 1045 steel, because it’s biodegradable and reduces petroleum-based cutting fluids.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".