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Record W4413049060 · doi:10.1016/j.jmrt.2025.07.258

Microstructural evolution and mechanical behavior of resistance spot welded FeCrNiSiB advanced high-strength steel

2025· article· en· W4413049060 on OpenAlexfundno aff
Mohammad Hossein Amini-Chelak, Hossein Aliyari, Reza Miresmaeili, Hamidreza Shahverdi, Mohsen Askari-Paykani

Bibliographic record

VenueJournal of Materials Research and Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsnot available
FundersQueen's University
KeywordsMaterials scienceSpot weldingWeldingComposite materialMetallurgyHigh strength steelMicrostructureMechanical strength

Abstract

fetched live from OpenAlex

This study examines the microstructural evolution and mechanical performance of resistance spot welded joints in a newly developed FeCrNiSiB advanced high-strength steel (AHSS). The effects of key welding parameters—current (4–7 kA), weld time (9–21 cycles), and electrode force (2–4 kN)—on nugget formation and failure behavior were systematically investigated through 24 welding trials. Lobe curves were constructed at three force levels to define optimal processing windows, showing narrower weldability ranges at higher forces. Microstructural analysis revealed a dendritic austenitic matrix interspersed with eutectic boride phases (M 2 B) in the fusion zone (FZ), while the partially melted zone (PMZ) exhibited partially liquefied grains with similar eutectics. Hardness in the FZ reached 1.5 times that of the base metal due to boride enrichment. Tensile-shear tests indicated a failure mode transition—from interfacial (IF) to partial interfacial (PIF), and eventually to partial thickness–partial pullout (PT-PP)—as nugget size increased. For welds with nugget diameters above 4 mm, joints exceeded the minimum tensile-shear strength requirement by approximately 14%. These findings demonstrate the influence of welding parameters on microstructure, hardness, and localized mechanical response in this novel boron-alloyed AHSS.

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)0.001
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 score0.032
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.301
Teacher spread0.291 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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