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Record W4416884495 · doi:10.37665/srykpdk64515

Fracture of Lead-Free Solder Joints As A Function of Strain Rate, Local End Geometry and Thickness

2013· article· W4416884495 on OpenAlexaff
Amir Nourani, J.K. Spelt

Bibliographic record

VenueSoldering and Reliability Conferences · 2013
Typearticle
Language
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSolderingJoint (building)Fracture (geology)Fracture toughnessStrain energyStrain (injury)Strain rateStrain energy release rate

Abstract

fetched live from OpenAlex

ABSTRACT The fracture of SAC305 solder was investigated as a function of strain rate using Cu-solder-Cu double cantilever beam (DCB) specimens joined with a series of 2 mm long discrete solder joints of 150 m thickness. The joints were then fractured with various strain rates under mode I and mixed-mode loading conditions. The failure of each joint in the DCB was accompanied by a sharp drop in the applied load. These maximum loads were used to calculate the initiation critical strain energy release rate, G ci , of the solder joints using a finite element model. The results showed a substantial increase of about 75% in the solder joint fracture toughness at strain rates of 0.05 to 1 s -1 compared to that under quasi-static loading conditions. This trend suggests that the solder G ci increases rapidly with strain rate whereas the G ci of the IMC is relatively independent of strain rate. Negligible changes in G ci were measured when the solder joint thickness was increased to 400 m. The dependence of crack initiation on the local shape of the solder joint at its end was investigated by fabricating the 2 mm long joints with either a square end (using Kapton tape) or a rounded end (using an embedded wire). Interestingly, these two local geometries produced almost identical values of G ci , suggesting that initiation was not a strong function of the shape of the solder joint.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.217
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 teacher head, not a consensus.

Study designOther design
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
Published2013
Admission routes1
Has abstractyes

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