A Hybrid RSM–PSO Approach for Enhancing Machining Performance in Nanofluid-Assisted Hard Turning
Bibliographic record
Abstract
This study presents a hybrid Response Surface Methodology (RSM) and Particle Swarm Optimization (PSO) framework to optimize hard turning of SKD11 steel under Minimum Quantity Lubrication (MQL) using a novel Al₂O₃-SiO₂ hybrid nanofluid in canola oil.Conducted with a CBN insert, experiments evaluated surface roughness (Ra) and material removal rate (MRR) across cutting speeds (60-100 m/min), feed rates (0.10-0.15 mm/rev), depths of cut (0.2-0.6 mm), and nanoparticle concentrations (Al₂O₃: 0-2 wt.%, SiO₂: 0-1 wt.%).A predictive RSM model (R² = 99.84%,p < 0.05) was developed for Ra.Single-objective PSO optimization yielded a minimum Ra of 0.5443 µm.Multi-objective optimization achieved a trade-off solution with Ra = 0.584 µm and MRR = 5230 mm³ /min, demonstrating the balance between surface quality and productivity.These results confirm the potential of hybrid nanofluid-assisted MQL combined with a hybrid RSM-PSO optimization algorithm in enhancing machining performance and supporting sustainable manufacturing practices in die and mold applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".