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Welding of Automotive Aluminum Alloys by Laser Wobbling Processing

2016· article· en· W2551041952 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

VenueMaterials science forum · 2016
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsCentre Casa
Fundersnot available
KeywordsMaterials scienceWeldingLaser beam weldingHeat-affected zoneButt weldingAutomotive industryComposite materialButt jointElectric resistance weldingPorosityAluminiumMetallurgyEngineering

Abstract

fetched live from OpenAlex

The scope of this paper is to examine the improvement from laser welding by an innovative beam wobbling head towards the welding of tailored blanks parts, widely used in automotive to develop different stiffness aluminum components. For this purpose, butt joints and overlapping joints were produced from sheets made out of two industrial grades, i.e. AA-6082 T6 and AA-5754 H111 of different thickness. The technique was evaluated both with and without the use of a filler wire (AA-5556). The qualification of the welding process encompassed Non Destructive Testing (NDT) and mechanical testing. The results indicate that butt joints tend to fail within the base material (BM) of sheet with smaller thickness. On the contrary, the shear tests on lap joints highlighted a rupture mode occurring in the heat affected zone (HAZ) of the thin sheet. Remarkably, the wobbling process generally allows avoiding porosity when combined with an optimized set of welding parameters. Yet, a residual porosity was always detected in lap joints, varying with the size of the fused zone.

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)0.001
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 score0.007
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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