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Record W2327094533 · doi:10.2351/1.4944101

Laser localized coating of corrosion resistant metal over a steel weld bead

2016· article· en· W2327094533 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 · 2016
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsMagna International (Canada)
Fundersnot available
KeywordsMaterials scienceCoatingWeldingMetallurgyCorrosionLaser beam weldingGalvanic corrosionGalvanic cellJoint (building)Composite materialSheet metalStructural engineering

Abstract

fetched live from OpenAlex

Coated steel sheet is one of the most important raw materials for the automotive industry. The commonly used Al or Zn based coatings on steel sheet provide a physical barrier and/or galvanic protection, and thus prevents a corrosive attack of the steel substrate. In order to make complex component or assembly, several steel sheets of various sizes, shapes, or thicknesses are welded together before or after being formed. Unfortunately, the process of welding together the precoated steel sheet pieces results in the formation of a weld joint that is devoid of anticorrosion protection. Re-formation of the protective coating over the weld joint could greatly improve the final product's quality in terms of resistance to corrosion. In this investigation, recoating of aluminum-silicon over laser weld joints has been explored by laser beam heating and powder injection. Precise coating over the weld joint was achieved. The produced coating on the welding bead is smooth when proper processing parameters are employed.

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 score0.335
Threshold uncertainty score0.246

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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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