Investigating Corrosion and Surface Hardness of Al6061 in Machining Fluids with Variable CNT Concentrations
Bibliographic record
Abstract
This project investigates the corrosion tendency of various machining fluids on Al6061 alloy for a sustainable cutting process.Also, the surface of the machining workpiece was analysed to understand the effect of the carbon nanotubes (CNTs) on the surface morphology that assists the cutting conversion of the sliding friction to the rolling process.The study used a two-step process to create machining fluid with CNT nanoparticles at different percentage concentrations.The soluble oil (sample B1), mineral oil (sample B2), 0.2-g/l-CNTs nano-mineral machining fluid (sample B3), 0.4-g/l-CNTs nano-mineral machining fluid (sample B4), and 0.6-g/l-CNTs nano-mineral machining fluid (sample 5) makes up the corrosion study medium.The corrosion study was conducted using polarisation resistance and potentiodynamic corrosion techniques.After the corrosion study, the Vickers micro-hardness machine was employed to evaluate the surface hardness of the normal face region subjected to the corrosion study.The SEM and EDS were also used to investigate the microstructure and elemental composition of the machining fluid impacts on the Al 6061 alloy.The results show that the soluble oil has a high corrosion rate of 0.03590591 (mm/year), with a list polarisation resistance rate of 8,432.83(Ω) compared to the other machining fluid employed in this study.Furthermore, the 0.6-g/l-CNTs nano-mineral machining fluid has the lowest corrosion rate of 0.0013197 (mm/year) and the highest polarisation resistance of 32,941.67(Ω).The 0.6-g/l-CNTs presence in the mineral oil increases the rheological and tribological properties, which results in an outstanding performance in the corrosion study.Furthermore, the CNTs hardened the surface of the workpiece due to the carbon element deposition during the implementation of the cutting fluid.
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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".