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Record W6987806857

Ultrasonic evaluation of friction stir welds and dissimilar intermixing using synthetic aperture focusing technique

2009· other· en· W6987806857 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2009
Typeother
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFriction stir weldingWeldingUltrasonic sensorButt weldingAerospaceUltrasonic weldingButt jointAluminium
DOInot available

Abstract

fetched live from OpenAlex

Friction stir welding (FSW) is a recently developed solid-state joining process that uses a specially shaped rotating tool to produce lap or butt joints. At the National Research Council, an inter-institute collaboration was started in 2007 with the goal of exploiting the NDE expertise and applying it for the characterization of friction stir welds for various industrial applications. In particular, very good performance was obtained using ultrasonic immersion or laser-ultrasonics combined with the synthetic aperture focusing technique (SAFT) for detecting lack of penetration in butt joints, discontinuities such as wormholes and hooking in lap joints. Dissimilar metal welds of aluminum and magnesium by FSW are also considered for automotive and aerospace applications. Complex vortex flows are produced during the FSW process that may create intercalated lamellar structures with the possible formation of intermetallic compounds, causing variable hardness and degradation in mechanical properties. A modified version of SAFT that takes into account the difference of ultrasonic velocity in the joint between that of Al and Mg has been developed to study the dissimilar intermixing. Welded samples in the butt configuration with different welding speeds and seam offsets are tested using the immersion technique with the modified SAFT. Results will be presented for both defect detection and weld characterization, and the capabilities and limitations will be discussed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.840
Threshold uncertainty score1.000

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 head0.015
GPT teacher head0.272
Teacher spread0.257 · 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.

Study designBench or experimental
Domainnot available
GenreMethods

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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