Cutting Fluid Effectiveness in the High-Speed Finish Machining of Inconel 718 Using a Whisker-Reinforced Ceramic Tool
Bibliographic record
Abstract
This paper aims to investigate the effectiveness of cutting fluid during the high-speed face-turning of superalloy Inconel 718 using chamfered whisker-reinforced ceramic inserts. It addresses this topic by providing a comprehensive understanding of the machinability of Inconel 718 under both dry and wet conditions through analytical friction modeling and a detailed analysis of the chip formation process. Two new indexes, named the Area Function (AF) and the Shape Function (SF), were derived to assess the serration intensity of the chips. Particular attention was paid to the interaction between the cutting speed and the cutting fluid. The results showed that wet conditions promote uniform chip formation, more stable forces, a lower coefficient of friction, and the absence of notch wear. At low cutting speed (60 m/min) and dry machining results in high serration intensity (SF = 0.7) and segmentation frequency (fseg = 22.08 kHz) compared to the SF of 0.4 and fseg = 19.69 kHz in wet conditions. The segmentation frequency increases significantly with cutting speed, reaching 71.03 kHz and 63.32 kHz at a cutting speed of 225 m/min for dry and wet conditions, respectively. It was also found that the rate of increase in the tangential force was lower (20.49 N/s) when using cutting fluid at a high cutting speed (225 m/min) compared to dry conditions (27.37 N/s).
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 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".