(Digital Presentation) On the Effect of Sizing on Electrochemical Response and Localized Corrosion Behavior of Alumix 123 P/M Alloy
Bibliographic record
Abstract
Powder metallurgy (PM) as an additive technique for fabricating parts has been used by many industries such as automotive, aerospace, and medical. However, it has been centered mostly around automotive industry which has benefited from large volume production, low cost, and near-net-shape manufacturing that comes with PM. High demand for producing fuel-efficient and light vehicles in automotive industry provides a driving force for extensive research activity on PM of aluminum alloys. Among different aluminum alloys, PM of 2xxx series are among the best choices due to their good sinterability and mechanical response. But industrial PM parts need post processing treatments such as sizing to meet the real-world applications. While sizing is widely used in industry to provide tighter tolerance, it can also help improving density, fatigue behavior, yield strength, and hardness. But there were very few studies on the effect of sizing on electrochemical response. In this study we produced Alumix 123 PM alloy using a press-and-sinter process and then sizing was applied on some of the samples to compare their long-term electrochemical behavior. The changes in the surface profile and roughness of the samples were investigated using confocal laser scanning microscopy. Electrochemical impedance spectroscopy (EIS) as a powerful technique for investigating long-term electrochemical behavior has been used to find the effect of sizing on corrosion response of Alumix 123 PM. In addition, effect of sizing on localized corrosion and pitting tendency was thoroughly discussed using electrochemical polarization results, scanning electron microscope images, and EIS outcomes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.181 | 0.032 |
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".