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Record W4214860548 · doi:10.18280/mmep.090107

Numerical Solution for Electronic Assemblies Subjected to Mechanical Bending

2022· article· en· W4214860548 on OpenAlexvenueno aff
Mohammad A. Gharaibeh, Aseel A. Almohammad

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldEngineering
TopicComposite Structure Analysis and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsBendingStiffnessBending stiffnessWork (physics)Structural engineeringFinite element methodMaterials scienceBoundary value problemNumerical analysisLayer (electronics)Composite materialMechanical engineeringEngineeringMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

This work introduces high-accuracy numerical solution for the two elastically coupled beams subjected to mechanical bending problem. Finite difference method (FDM) was considered to solve the governing equations along with the boundary conditions of the structure. The validity of this solution was ensured and tested with literature data. Finally, the influence of the key structural parameters of the problem, such as the relative stiffness between the beams as well as the elastic layer, was thoroughly discussed with the specific attention on its application in electronic assemblies subjected to mechanical bending. The numerical findings showed that for stiffer beams and compliant layer, the axial deformations of the layer are lower which can be reflected as lower solder stresses and hence more reliable designs of electronic devices.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.197
Teacher spread0.186 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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Same venueMathematical Modelling and Engineering ProblemsSame topicComposite Structure Analysis and OptimizationFrench-language works237,207