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Record W4232070024 · doi:10.32920/ryerson.14657310.v1

Microstructural characterization and mechanical properties of spot welded dissimilar advanced high strength steels

2021· preprint· en· W4232070024 on OpenAlexaff
Muhammad Sohaib Khan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCharacterization (materials science)Materials scienceWeldingHigh strength steelSpot weldingMetallurgyComposite materialNanotechnology

Abstract

fetched live from OpenAlex

<p>Increased use of advanced high strength steels (AHSS) in resistance spot welding is necessary for manufacturing safe and affordable vehicles. A significant body of work has been completed to document the resistance spot weldability of AHSS. However, In automotive applications, the dissimilar material combinations are very common In automotive construction. There is no literature regarding the resistance spot welding of dissimilar materials DP600 and HSLA350 steels. The objective of this study was to investigate the weldability and mechanical properties of resistance spot welds between HSLA350 and DP600 steels. The dissimilar material spot weld performance was different than the similar material spot welds in each of the HSLA350 and DP600 steels and exhibited different heat affected zone hardness. The DP600 weld properties played a dominating role on the microstructure and mechanical properties of the dissimilar material spot welds. However, the fatigue performance of the dissimilar welds was similar to that of the HSLA welds. Fatigue tests on the dissimilar materials spot welds showed that at a given stress amplitude the 5.5 mm diameter nugget exhibited higher fatigue strength than the 7. 5 mm diameter nugget.</p>

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 score0.026
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
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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
Published2021
Admission routes1
Has abstractyes

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