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Record W2465344432 · doi:10.1515/amm-2016-0172

Analysis of Using Acoustic Microscopy to Evaluate Defects in Spot Welding Joints

2016· article· en· W2465344432 on OpenAlexfundno aff
Marcin Korzeniowski, Beata Białobrzeska

Bibliographic record

VenueArchives of Metallurgy and Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsnot available
FundersNarodowe Centrum Badań i RozwojuUniversity of Windsor
KeywordsWeldingMaterials scienceAcoustic microscopyMicroscopySpot weldingJoint (building)Optical microscopeNondestructive testingComposite materialMetallurgyOpticsStructural engineeringScanning electron microscopeEngineering

Abstract

fetched live from OpenAlex

Abstract The article presents the possibilities of using acoustic microscopy to evaluate defects in resistance spot welding joints. For this purpose, the welded joints were made from two grades of aluminium plates EN AW5754 H24 and EN AW6005 T606, which were then subjected to non-destructive testing using acoustic microscopy and conventional destructive testing using traditional light microscopy techniques. Additionally, the study examined the influence of the typical contaminants found in industrial conditions on the quality of the 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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.016
GPT teacher head0.286
Teacher spread0.269 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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