MétaCan
Menu
Back to cohort
Record W2048128315 · doi:10.1063/1.3362180

FINITEELEMENTMODELING OFWAVEPROPAGATION DURINGIN-LINEULTRASONICMONITORING OFSPOTWELDS

2010· article· en· W2048128315 on OpenAlexaff
J. Kocimski, Paweł Kustroń, W.G. Arthur, Anthony C. Karloff, Waldo Perez Regalado, Andriy M. Chertov, Andrzej Ambroziak, Roman Gr. Maev, Donald O. Thompson, Dale E. Chimenti

Bibliographic record

VenueAIP conference proceedings · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsFinite element methodWeldingUltrasonic sensorIndentationAcousticsWave propagationComputer scienceMechanical engineeringStructural engineeringEngineeringOpticsPhysics

Abstract

fetched live from OpenAlex

This paper presents the problem of ultrasonic wave's propagation in multilayered media. Using the Finite Element Method (FEM) a simulation of wave propagation was developed for real time quality control of spot welds. The simulation allows study change in wave propagation patterns through the media at different moments of welding. In particular, the arriving time of the wave passing through different temperature fields is investigated. FEM analysis is a very good way to understand the influence of temperature distribution and indentation on the time of flight (TOF) through the weld. The article presents a comparison between FEM simulations and experimental results. The analysis indicates a correlation with experimental data, which confirms that the models were prepared correctly and that certain simplifications had no significant influence on the results. FEM modeling is a suitable technique to optimize the welding system geometry (for example, shape of the electrode) and to visualize the behavior of ultrasonic wave propagation inside chosen elements of the welding set‐up.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueAIP conference proceedingsSame topicAdvanced Welding Techniques AnalysisFrench-language works237,207