MétaCan
Menu
Back to cohort
Record W4416884552 · doi:10.37665/srqmrkk46393

Microstructure and Hardness of Bi-Containing Solder Alloys After Solidification and Ageing

2014· article· W4416884552 on OpenAlexaff
André M. Delhaise, L. Snugovsky, Doug D. Perovic, Polina Snugovsky, Eva Kosiba

Bibliographic record

VenueSoldering and Reliability Conferences · 2014
Typearticle
Language
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsHain Celestial (Canada)University of Toronto
Fundersnot available
KeywordsMicrostructureRockwell scaleSolderingAlloyPrecipitation hardeningQuenching (fluorescence)Indentation hardnessHardening (computing)

Abstract

fetched live from OpenAlex

ABSTRACT This paper is focused on the microstructure and hardness analysis of new bismuth (Bi) containing alloys that have lower melting and process temperature than the conventional SAC305 solder alloy. One of the main SAC solder drawbacks is fast coarsening of microstructure resulting in degradation of the mechanical and thermomechanical properties during ageing. Seven Bi-containing alloys were used as listed in Table 1. Samples were aged for 25 and 100 hours at 100°C. Alloy hardness was measured after solidification ageing and quenching using a Rockwell hardness tester (HR15T scale with a 1/16” carbide ball indenter). The microstructures after solidification and after ageing were compared using scanning electron microscopy (SEM). The microstructures undergo significant changes upon ageing. Bi particles become uniformly distributed in the Sn matrix. While the hardness of SAC alloys is reduced after annealing, the hardness of these experimental alloys increases. The hardness gradually increases with increasing Bi content, as well as with increasing Ag content. The effect of solid solution strengthening and precipitation hardening mechanisms on microstructure and properties is discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.216
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueSoldering and Reliability ConferencesSame topicElectronic Packaging and Soldering TechnologiesFrench-language works237,207