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Record W1995564898 · doi:10.2351/1.3485596

Laser lap welding of zinc coated steel sheet with laser-dimple technology

2010· article· en· W1995564898 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Laser Applications · 2010
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsMagna International (Canada)
Fundersnot available
KeywordsMaterials scienceLaser beam weldingWeldingElectric resistance weldingButt weldingSheet metalArc weldingCoatingMetallurgyGas metal arc weldingComposite material

Abstract

fetched live from OpenAlex

Laser beam welding technology has been widely used to weld automobile components, especially for tailored blank welding. In order to provide best corrosion resistance to the welded sheet metal parts, zinc coated steel sheets are normally used. The zinc coating poses no issues to the butt joining of the sheet metals. However, when laser welding technology is applied to lap joint of these sheets, the welding process is not straightforward. Special techniques must be employed to allow the venting of the zinc vapor that is generated at the interface between the paired sheets. Many efforts have been attempted around the world trying to develop a practical technique for laser lap welding of zinc coated steel sheets. Most of the developed technologies has some success but with limitations—extra cost for equipment and process or limited convenience of implementation. In this paper, a new concept of laser lap welding with laser dimpling technology is described. This lap welding technique was implemented successfully in a robotic laser welding system in the laboratory environment and is capable of incorporation into manufacturing processes.

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.

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 score0.224
Threshold uncertainty score0.414

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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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