The Effects of Bi and Aging on the Microstructure and Mechanical Properties of Sn-Rich Alloys
Bibliographic record
Abstract
ABSTRACT This paper examines the effects of Bi on the microstructure and hardness of Sn-Bi and Sn-Cu-Bi alloys subjected to ageing treatments at room and elevated temperatures. One main concern with SAC alloys that has led to research of Bi-containing alloys is the degradation of mechanical and thermomechanical properties due to the coarsening of microstructure during aging, and in earlier studies, the inclusion of Bi in the alloy results in a uniformity of microstructure and an increase in alloy hardness. The goal of this paper and ongoing research is to investigate whether these trends hold for binary alloys, and to understand what mechanisms are responsible for these effects. Four alloys - Sn-1Bi, Sn-5Bi, Sn-0.7Cu-1Bi, and Sn-0.7Cu-5Bi - were aged at room temperature for 10 days or 28 days. Two of these, Sn-1Bi and Sn-5Bi, were aged at 100°C for 7 days, and were all cooled in air. The microstructure of the samples after solidification and aging were compared using Scanning Electron Microscopy (SEM). Alloy hardness after solidification and aging was measured using a Rockwell hardness tester HR15X with a ¼” Carbide ball indenter. In alloys containing Bi precipitates, these particles became more uniformly distributed with aging. Hardness was observed to not undergo any significant changes after aging, which differs significantly from SAC alloys.
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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".