Optimising subsurface integrity and surface quality in mild steel turning: A multi-objective approach to tool wear and machining parameters
Bibliographic record
Abstract
This study investigates the impact of machining parameters and tool dynamics on mild steel degradation of surface quality and subsurface, including heat effects, deformation, and microstructural changes that lead to microcracks and work hardening. Firstly, we examined the individual impacts of cutting velocity (Vc), feeding rate (f), and depth of cut (ap) on surface roughness and surface topography. Secondly, an examination was conducted to assess the influence of tool wear on the morphology of the turning surface using the white light interferometer (ZYGO). Finally, this study employs Grey Relational Analysis (GRA), Data Environment Analysis Ranking (DEAR), and Multi-objective Optimization based on Ratio Analysis Method (MOORA) optimization techniques with S/N ratios to refine 3D surface roughness (Sa, Sz, Sq) and material removal rates (MRR) in mild steel turning using a CVD-coated carbide tool. Key findings reveal that increasing Vc reduces surface roughness and improves morphology, while higher f and ap deteriorate both. Tool wear progresses through three stages, with the poorest surface quality occurring in the final stage. The results showed that cutting speed is the most influencing parameter on surface roughness in wet (43.37%) and dry (56.66%) turning, followed by feed rate (wet: 6.90%, dry: 7.71%) and depth of cut having minimal impact (wet: 2.04%, dry: 0.12%). The optimal machining parameters, determined as Vc = 125.6 m/min, f = 0.35 mm/rev, and ap = 0.7 mm, demonstrate the efficacy of the optimization techniques in achieving enhanced surface quality and making a significant contribution to the field of machining and manufacturing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".