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Record W6920121735 · doi:10.60692/mgj5b-v8w89

Microstructure-property relations in as-atomized and as-extruded Sn-Cu (-Ag) solder alloys

2016· article· en· W6920121735 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2016
Typearticle
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEutectic systemIntermetallicMicrostructureIndentation hardnessUltimate tensile strengthAlloySolderingDendrite (mathematics)Extrusion

Abstract

fetched live from OpenAlex

Rapidly solidified Sn-based solder alloys can provide metallurgical features such as low segregation and fine intermetallic compounds (IMCs). These features can be obtained in a controlled way by Impulse Atomization that provides powders of various sizes corresponding to a variety of cooling rates and undercoolings to which diverse microstructures are associated. In the present investigation, rapid solidification of Sn-0.7 wt%Cu and Sn-0.7 wt%Cu-3.0 wt%Ag alloys have been examined through the production of a wide size range of impulse atomized powders. The microstructures and hardness resulting from the generated powders have been compared with those of directionally solidified (DS) specimens. Regular cells > dendrites and reverse dendrites > cells transitions were identified, and high-speed eutectic cells were found to prevail for the examined Sn-0.7 wt%Cu powders of size smaller than 300 μm. It is shown that the Vickers microhardness of the ternary Sn-3.0 wt%Ag-0.7 wt%Cu alloy is directly influenced by both the presence of tertiary dendrite arms (λ3) and the cooling rate/powder size dependent eutectic fraction. Also, compaction and extrusion of the atomized powders were carried out in order to consolidate the samples so that tensile tests could be carried out. Tensile strength and ductility of samples corresponding to different powder sizes and compositions were thus measured and the results are found to be consistent with their microstructures.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.184
Teacher spread0.172 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2016
Admission routes1
Has abstractyes

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