Turning of Inconel 718 Using Liquid Nitrogen: Multi-objective Optimization of Cutting Parameters Using RSM
Bibliographic record
Abstract
Abstract The objective of this research was to investigate the effectiveness of application of liquid nitrogen (LN 2 ) in turning of Inconel 718 compared to flooded cutting and select suitable LN 2 cutting parameters using response surface methodology (RSM). The results of turning experiments conducted by spraying LN 2 to the cutting area of Inconel 718 bar showed that using either low or high cutting parameters, cutting performance of Inconel 718 under the cryogenic condition was generally worse than the flooded cutting. However, using the medium cutting parameters the cutting performance was as good as flooded cutting showing the values of 90 N of cutting force, 60 µm of flank wear and 0.5-0.6 µm of surface roughness (Ra). These parameters were further optimized using desirability function of RSM to determine the set of parameters that provide the lowest cutting force, flank wear and Ra and the highest material removal rate (MRR) under cryogenic cutting. Analysis of variance (ANOVA) performed on the developed regression models showed that cutting speed was the significant factor on cutting force. Feed rate was the most influential parameter on flank wear. Feed rate and depth of cut was significant factors affecting Ra. Multi-objective optimization showed that a cutting speed of 87 m/min, a feed rate of 0.06 mm/rev and a depth of cut of 0.37 mm constituted the optimum cutting parameters for achieving the, cutting force of 78 N, flank wear of 58 µm, Ra of 0.49 µm and the MRR of 1.97 cm 3 /min under cryogenic cutting condition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".