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Record W4413481152 · doi:10.3390/met15090940

Dissimilar Resistance Spot Weld of Ni-Coated Aluminum to Ni-Coated Magnesium Using Cold Spray Coating Technology

2025· article· en· W4413481152 on OpenAlexaff
Mazin Oheil, Dulal Chandra Saha, Hamid Jahed, A.P. Gerlich

Bibliographic record

VenueMetals · 2025
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCoatingSpot weldingMaterials scienceMetallurgyMagnesiumGas dynamic cold sprayAluminiumWeldingComposite material

Abstract

fetched live from OpenAlex

Direct fusion welding of aluminum (Al) to magnesium (Mg) results in the formation of brittle intermetallic compounds (IMCs) that significantly restrict the application of these joints in structural applications. In this study, cold spray, a promising solid-state coating deposition technology, was employed to introduce a nickel (Ni) interlayer to facilitate joining of Al to Mg sheets by means of resistance spot welding (RSW). The ability of cold spraying to deposit metallic powder on the substrate without melting proves beneficial in mitigating the formation of the Al-Mg IMCs. The Ni-coated coupons were subsequently welded via resistance spot welding at optimized parameters: 27 kA for 15 cycles in two pulses with a 5-cycle inter-pulse delay. Scanning electron microscopy confirmed metallurgical bonding between the Al, Mg, and Ni coatings in the fusion zone. It is shown that the bonding between the three elements inhibits the formation of deleterious IMCs. Tensile shear testing showed joint strength exceeding 4.2 kN, highlighting the potential of the proposed cold spray RSW approach for dissimilar joining in structural applications.

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 categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
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 head0.013
GPT teacher head0.258
Teacher spread0.245 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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