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Record W4387421886 · doi:10.3390/met13101704

Effect of Laser Welding Parameters with Different Fillers on Solidification Cracking and Mechanical Properties of AA7075

2023· article· en· W4387421886 on OpenAlexaff
Mohammed Alkhabbat, François Nadeau, Fatemeh Mirakhorli, Thiên-My Dao, Xuan-Tan Pham

Bibliographic record

VenueMetals · 2023
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsNational Research Council CanadaÉcole de Technologie Supérieure
Fundersnot available
KeywordsMaterials scienceWeldingMicrostructureComposite materialUltimate tensile strengthLaser beam weldingHeat-affected zoneFusion weldingAlloyCrackingMetallurgy

Abstract

fetched live from OpenAlex

AA7075 is considered a ‘non-weldable’ alloy using fusion welding methods. In this study, laser welding is applied in pulse mode to weld 2 mm thick AA7075 aluminum alloy plates using different fillers. The aim is to identify the influence of welding parameters and fillers on solidification cracking susceptibility during laser welding using the circular patch test (CPT). X-ray radiography was used to detect and measure cracks in the CPT samples. Furthermore, the effects of the laser welding process and chemical composition of fillers on the accumulated crack length (CCL), microstructure, and mechanical properties were investigated. Moreover, the mechanical behavior and local deformation of the fusion zone (FZ) were investigated using micro-flat tensile tests with digital image correlation. The mechanical properties of the FZ were correlated with the CCL as well as with the microstructure of the FZ, which was investigated experimentally. The results show that the chemical composition of fillers and welding speed affect the CCL of solidification cracks. Changes in the microstructure were observed within the fusion zone, and the structure became uniform and finer with the formation of Mg2Si and magnesium-rich, copper, and zinc (η-phase) particles.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.372

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 head0.017
GPT teacher head0.213
Teacher spread0.196 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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