The Effect of Multi-Walled Carbon Nanotubes Additives on the Tribological Properties of Austempered AISI 4340 Steel
Bibliographic record
Abstract
Due to a combination of optimal properties such as great strength, high hardness, good process ability, and good mechanical properties, AISI 4340 steel is widely used in many critical industrial applications such as nuclear, military, defense, and aerospace. It is also widely used in hydraulic forged machine tools, forged automotive crankshaft systems, shafts and gears, because of their improved characteristics, and its good tribological properties. The purpose regarding this work is to check the tribological characteristics of austempered AISI 4340 steel while dry and lubricated with machinery oil of SAE 30 grade as base oil. As received, AISI 4340 steel samples have been austempered to four definitely austenitic phase temperatures (850℃, 900℃, 1000℃, and 1050℃) for 90 minutes before being immersed in a mixture of potassium nitrite and sodium nitrite at 400℃ for 45 minutes. Friction and wear tests were then performed on austempered samples. Multi-walled carbon nanotube particles were blended at weight concentrations of 0.055, 0.1, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, and 0.45 with typical machinery oil of 30 grade as base lubricant oil. A pin on the disc wear configuration was used in the experimental investigation. The use of Multi-Walled Carbon Nanotube (MWCNTs) additives in the base oil resulted in a decrease in both friction coefficients and wear rates values when compared to typical base oil lubricant. The results also showed a reduction in both friction coefficients and wear rates as the sample's austempering temperatures were raised. Sliding surfaces were also photo micro graphed, and when the volume concentrations of Multi-Walled Carbon Nanotube particles in the normal base oil lubricant were increased, smoother surfaces with less damage were shown.
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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".