Effect of External Heat Source on Surface Roughness in Turning of Al/SiC Metal Matrix Composites: A Comparative Study of Optimization Methods
Bibliographic record
Abstract
This research experimentally examines the impact of external heat sources, specifically carburizing and oxidizing heat sources, on the surface quality obtained during the turning of Al/SiC metal matrix composites (Al/SiC-MMCs) on a lathe.A 3-level, 3-factor full factorial design was applied by considering cutting velocity, cutting feed, and cutting depth as variables.According to the experimental findings, empirical power-law analytical models were established to evaluate surface roughness.To optimize the cutting variables, the derived models and associated constraints were subjected to four metaheuristic optimization algorithms such as Differential Evolution (DE), Whale Optimization Algorithm (WOA), Cuckoo Search (CS), and Teaching Learning Based Optimization (TLBO).This study aims to find the most effective combination of cutting velocity, cutting feed, and cutting depth that would improve surface quality and enhance the Material Removal Rate (MRR).Experimental outcomes demonstrate that carburizing flame-assisted turning substantially improves surface quality (overall surface roughness decreased by 17.25%) compared to dry machining and turning assisted by an oxidizing flame.Among the optimization techniques, TLBO achieved the best optimization performance, consistently producing the least surface roughness value of 3.403, with cutting speed converging to 94 m/min, feed rate to 0.113 mm/rev, and depth of cut between 0.34 mm and 0.75 mm.Statistical analysis further confirmed TLBO's superiority, yielding the lowest mean fitness value (3.403), lowest standard deviation (0.005), and highest stability (95% of runs within ±0.005 of the best value).TLBO proved to be the most reliable and effective method for improving surface finish in machining.
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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 | 0.000 |
| 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) | 0.001 | 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".