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Record W2895862052 · doi:10.2351/1.5062880

Ivestigating the weld depth behaviour using different observation techniques: X-ray, inline coherent imageing and highspeed observation during welding ice

2013· article· en· W2895862052 on OpenAlexaff
Meiko Boley, Peter Berger, Paul J. L. Webster, Rudolf Weber, C. Van Vlack, James M. Fräser, Thomas Graf

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsGeneral Dynamics (Canada)Queen's University
Fundersnot available
KeywordsWeldingMaterials scienceX-rayGeologyOpticsMetallurgyPhysics

Abstract

fetched live from OpenAlex

For a comprehensive understanding of the deep-penetration laser-welding process, it is of fundamental interest to understand the dynamic behavior of the capillary. The X-ray system of the Institut fuer Strahlwerkzeuge (IFSW) allows to record a two-dimensional projection of the capillary with frame rates up to 10 kHz. Hence the system is capable to gain information about the welding capillary, such as size and shape. However, the laser-welding process is a highly dynamic process with significant changes in time periods shorter than 0.1 ms. Queen’s University and Laser Depth Dynamics (LDD) have developed a sensor based on inline coherent imaging [1] which provides direct geometrical measurements of the keyhole depth and associated dynamics at rates >300 kHz with micron-scale precision. The technique is based on spectral domain low-coherence interferometry and is delivered through a camera port and combined co-axially with the process beam. The two measurement methods were set up in a combined experiment. The X-ray system recorded the shape and depth of the capillary with a spatial resolution of about 100 µm and a frame rate of 1 kHz, whereas the depth sensor from LDD provided 200 kHz at a single spot with axial resolution on the order of 10 µm. The present contribution compares the results of the two methods for steel and aluminum welds allowing new insights to the short-timescale behavior of the capillary. In order to understand the principal findings, the results were compared with high speed videos, taken during welding of the transparent material ice. Compared to the X-ray technique, a higher spatial resolution can be obtained at high repetition rates. At a first glance, it might be astonishing, that welding of ice can be compared with welding of metals. In a large number of experiments, however, we found that during welding of ice a lot of phenomena known from welding of metals (especially steel and aluminum) are also present during welding of ice, but can be observed much more clearly because of the low temperature and the transparency of the material.

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: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.882

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 communication0.0000.001
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 head0.028
GPT teacher head0.240
Teacher spread0.212 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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