Influence of porosity and alloy addition on the wear behaviour of sinter-forged C45 steel using Taguchi method
Bibliographic record
Abstract
Elemental powders of iron (Fe), molybdenum (Mo), and carbon (C) were mixed in a pot mill to obtain the compositions of C45, C45–1% Mo, and C45–2% Mo steels. They were then compacted and sintered. The sintered preforms had a density of 75% of the theoretical density (TD). Then, the sintered preforms were subjected to densification to obtain the two densities of 80% and 85% TD through forging. The sintered and densified preforms of the alloy steel were subsequently machined to obtain the required wear test specimens. The experiments were conducted on a pin-on-disc tribometer, conforming to ASTM G99 standards, on a rotating EN32 disc. Using Minitab 16 software, dry sliding wear experiments were planned using a L27 orthogonal array. The percentage TD of the specimens (%Theoretical density + %Porosity = 1), percentage Mo addition, load, and sliding velocity were taken as input parameters, and mass loss was the output parameter. It was observed that increasing the density of the alloy steel adversely affects the wear resistance of the alloy steel, and thus the mass loss is increased. The addition of Mo to the C45 steel improves the wear resistance irrespective of density, owing to hard-phase carbides present in the microstructure. Empirical correlations for mass loss with respect to input parameters were developed using regression analysis. The hardness of the alloy steel was directly related to the density of the alloy. Mo addition contributed to an increase in hardness of the alloy steel. It was observed from optical images of the wear pattern that the C45 steel is subjected to uniform wear, as an evenly spread wear track appeared in the images. On the other hand, it was observed that the C45–Mo-alloyed steel exhibited non-uniform wear because of hard-phases present in the microstructure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".