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Record W4236782574 · doi:10.32920/ryerson.14656635.v1

Microstructure and Mechanical Properties of Welded Advanced Materials for Automotive Applications

2021· preprint· en· W4236782574 on OpenAlexafffund
Wen Xu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaInternational Zinc AssociationUniversity of WaterlooArcelorMittal
KeywordsMaterials scienceWeldingUltimate tensile strengthComposite materialMicrostructureMetallurgySpot weldingAdhesiveLayer (electronics)

Abstract

fetched live from OpenAlex

Growing use of advanced materials (advanced high strength steel DP980 and ultralight-weight magnesium alloys) and innovative joining techniques (new-generation laser welding technology and weld bonding technique) is crucial for better fuel economy and lower CO2 emissions in automotive manufacturing. Microstructures and mechanical properties of fiber laser welded high strength low alloy and DP980 steel joints, weld-bonded Mg/Mg and Mg/steel joints and adhesive-bonded Mg/Mg joints were studied. Tempered martensite and welding concavity were observed in fiber laser welded DP980 joints which reduced fatigue resistance, while both HSLA and DP980 joints showed a superior tensile strength. Weld-bonded Mg/Mg and Mg/steel joints with an adhesive layer were significantly stronger than resistance spot welded Mg/steel joints. Reducing bonding length on weld bonded Mg/Mg joints led to a higher maximum tensile shear stress, both tensile and fatigue strength were slight lower than that of adhesive bonded Mg/Mg joints, while ability of energy absorption was equivalent. The tensile properties reduced at a higher temperature (90°C) but it increased at a lower temperature (-40°C).

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: Methods · Consensus signal: none
Teacher disagreement score0.568
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.244
Teacher spread0.233 · 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
GenreMethods

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
Published2021
Admission routes2
Has abstractyes

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