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Record W4416885286 · doi:10.37665/srmdqxm15032

Predicting the Strength of Solder Joints using Cohesive Zone Modeling

2009· article· W4416885286 on OpenAlexaff
Siva Nadimpalli, J.K. Spelt

Bibliographic record

VenueSoldering and Reliability Conferences · 2009
Typearticle
Language
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSolderingCohesive zone modelFinite element methodJoint (building)Ball grid arrayFracture (geology)

Abstract

fetched live from OpenAlex

ABSTRACT The fracture of a lead-free solder joint (96.5Sn3.0Ag0.5Cu and copper) under mode I loading conditions was simulated using a cohesive zone model (CZM) implemented in ANSYS® finite element software. The CZM parameters were determined using mode I fracture data from experiments with double cantilever beam (DCB) solder joint specimens. These CZM parameters, along with the mechanical properties of the solder and the copper, were then used in the finite element simulations to predict the failure loads of solder joints in two configurations: (i) DCB joints and (ii) linear arrays of discrete 1-2 mm solder joints having various pitches. The latter specimens permitted an investigation of the parameters affecting load sharing among discrete joints such as those found in ball grid arrays. The model predictions were in good agreement with the experiments.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.025
GPT teacher head0.247
Teacher spread0.222 · 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 designSimulation or modeling
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
Published2009
Admission routes1
Has abstractyes

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