Experimental Investigation on the Performance of Different Cutting Fluids on Cutting Force During Turning of Duplex Stainless Steel-2205 under MQL Technique
Bibliographic record
Abstract
Duplex stainless steel (DSS)-2205 comes under hard-to-machine material owing to its inherent properties but more applications in severe working conditions. Hence, investigating the effect of cutting fluids and machining parameters is essential. In the present work, an attempt has been made with Minimum Quantity Lubrication (MQL) the investigate the performance of Deionized (DI) water, neat cut oil, and emulsified fluid on Cutting Force (CF) during turning of Duplex Stainless Steel (DSS-2205). The experiments were conducted based on face central composite design (CCF) in response surface methodology, varying speed, feed, and depth of cut in three levels. The Analysis of Variance (ANOVA) is to identify significant factors that influence the response. The results revealed that using emulsified fluid's cutting force gives better results than the DI water and neat cut oil. Feed rate is the most significant factor for emulsified fluid contribution was 53.61% for neat cut oil 48.89% and DI water 26.11%. It also reveals that the contribution of the depth of cut is slightly lesser than the feed rate. However, the contributions of cutting speed in all three Deionized (DI) water, neat cut oil, and emulsified fluid working fluids are negligible.
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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 | 0.000 |
| 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) | 0.000 | 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".