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Record W4234591533 · doi:10.32920/ryerson.14660508.v1

Reliability of soldered joints for aerospace applications

2021· preprint· en· W4234591533 on OpenAlexaff
Brigitte Desrochers

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAerospaceReliability (semiconductor)SolderingReliability engineeringTinMaterials scienceManufacturing engineeringMechanical engineeringComputer scienceEngineeringMetallurgyAerospace engineering

Abstract

fetched live from OpenAlex

Known to be highly reliable, Tin-Lead (SnPb) solders have long been used in commercial and aerospace electronic assemblies due to their ability to withstand thermo-mechanical fatigue. A number of constitutive and thermo-mechanical life models for SnPb soldered joints can be used to determine whether a given board design meets the reliability requirements of a system. Known to be highly reliable, Tin-Lead (SnPb) solders have long been used in commercial and aerospace electronic assemblies due to their ability to withstand thermo-mechanical fatigue. A number of constitutive and thermo-mechanical life models for SnPb soldered joints can be used to determine whether a given board design meets the reliability requirements of a system.Due to environmental and toxicological concerns, government legislations worldwide now limit the use of Pb in manufacturing processes. Therefore, the aerospace industry must understand the reliability of proposed Pb-free alternatives prior to using them in the aerospace applications. In order to improve the space-readiness of Pb-free solders, a well-rounded collection of space-specific test results must be compiled and test-verified predictive life models must be developed. Finally, quality control processes associated with manufacturing, handling, and repairing Pb-free solders will have to be created before the space industry can make the transition from SnPb to Pb-free solders.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score0.746

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 communication0.0000.000
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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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 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
Published2021
Admission routes1
Has abstractyes

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