Effects of Graphite Content and Temperature on Microstructure and Mechanical Properties of Iron-Based Powder Metallurgy Parts
Bibliographic record
Abstract
An experimental investigation was conducted to study the effects of graphite content and temperature on the microstructure and mechanical properties of iron-based powder metallurgy parts. The specimens were produced at two sintering temperatures, 600 °C and 1100 °C, respectively, and the graphite contents were 0.5%, 1%, 1.5% and 2%, respectively. The polished and etched specimens were examined by optical metallography (OM) and scanning electron microscopy (SEM). Brinell hardness of the sintered specimen was measured to evaluate the mechanical behavior, and the density and the porosity of the specimens were calculated to evaluate the compaction and sintering. The results show that: (1) as the graphite content increasing from 0.5% to 2%, the microstructure of the iron-based powder sintered specimen changes gradually from ferrite and a small amount of pearlite to pearlite and a small amount of ferrite, (2) with the sintering temperature increasing, the microstructure of the sintered interface becomes uniform, (3) with the graphite content increasing, the hardness of the iron-based powder sintered part grows obviously and (4) the densities of the specimens with different graphite contents at 1100 °C are higher than those at 600 °C, and with graphite content increasing, the porosity of the sintered specimen decreases.
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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 | 0.001 |
| 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".