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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 OpenAlexaff
Hongping Gu

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.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
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 head0.007
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

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 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

Citations27
Published2010
Admission routes1
Has abstractyes

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