Effect of precipitation hardening on the properties of 17-4ph stainless steel foams
Bibliographic record
Abstract
This paper presents the results of a study on the impact of precipitation hardening on the mechanical properties of 17-4PH stainless steel open-cell foams produced using a powder-metallurgy-based process patented by the National Research Council Canada (NRC). Pre-alloyed powder was used to manufacture stainless steel (SS) foams with either medium or high porosity by changing the nature of the organic binder used to process the porous materials. Some of these were kept in the as-sintered state, while others were submitted to the H900 precipitation hardening treatment frequently prescribed for 17-4PH stainless steels. Metallurgical and physical characterization was carried out on the resulting materials, along with mechanical testing at the micro (nano-indentation) and macro (compressive testing) scales. It was found that the Medium-Porosity (MP) and High-Porosity (HP) foams had very different morphologies, the HP foams having a delicate porous structure featuring thin sintered walls with many openings (a.k.a. windows) between the main cells, while the MP foams exhibited much thicker walls with few windows connecting the larger pores. As expected from these foam morphologies, the mechanical properties of MP foams were much higher than those of the more porous and delicate HP materials. For both foam types, the average mechanical properties were improved by the H900 treatment. However, a large scatter was observed on compressive properties versus relative porosity of individual specimens for both HP and MP foams, whose origin should be further investigated.
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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) | 0.002 | 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".