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Record W2897768106 · doi:10.2351/1.5056999

Studies on the laser spot welding of steel/magnet dissimiliar materials

2008· article· en· W2897768106 on OpenAlexaff
Baohua Chang, Xiaogang Li, Yuquan Ding, Hua Zhang, Dong Du, Y. Zhou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMetallurgical and Alloy Processes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceWeldingNeodymium magnetMetallurgySpot weldingJoint (building)MagnetBrittlenessHeat-affected zoneLaser beam weldingBase metalFiller metalElectric resistance weldingComposite materialArc weldingMechanical engineeringStructural engineeringEngineering

Abstract

fetched live from OpenAlex

The paper studies the laser spot welding of SPCC steel and NdFeB permanent magnet. Results show that during welding process the two metals quickly melt, mix and then solidify to form a weld that joins two specimens together. The hardness in the joint is not uniform: the heat affected zone (HAZ) has a lower hardness than NdFeB base metal; within the nugget, the region adjacent to the fusion line has the highest hardness while the middle part of nugget has the lowest hardness in the joint. Hot cracks are apt to occur at the interface between nugget and magnet base metal, whereat the cracks propagate and lead to joint failure during shear tests. The fracture is intergrannular and is a typical brittle fracture. To increase the joint strength and prolong the service life of welded components, it is necessary to take some metallurgical and technological measures to improve the bonding quality between nugget and magnet base metal.

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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.088
GPT teacher head0.289
Teacher spread0.202 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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