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Record W4404843693 · doi:10.1016/j.msea.2024.147619

In-situ dual force: A novel pathway to improving the mechanical properties of resistance spot welds

2024· article· en· W4404843693 on OpenAlexaff
Olakunle Timothy Betiku, Ali Ghatei Kalashami, Hassan Ghassemi-Armaki, E. Biro

Bibliographic record

VenueMaterials Science and Engineering A · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSpot weldingDual (grammatical number)In situMaterials scienceMechanical resistanceResistance (ecology)Composite materialMechanical engineeringEngineeringPhysicsWeldingArt

Abstract

fetched live from OpenAlex

Although in-situ post-weld heat treatment (PWHT) has been a viable method to modify resistance spot weld microstructure and improve joint mechanical properties, prevailing methodologies only employ post-weld current to initiate microstructural transformations . This study uses in-situ dual force (DF) and a PWHT current pulse to induce microstructural changes specifically at the edge of the fusion zone (FZ), a region prone to crack propagation . After a short cooling period following the welding cycle, the application of strain energy from the DF and thermal energy from the PWHT current resulted in the formation of new equiaxed grains via austenite recrystallization. The energy absorption capability of the weld improved by 39 % after the DF schedule and 85 % when DF was combined with a PWHT current. The changes in mechanical properties resulted from strain hardening induced by the DF schedule, while grain refinement from the combined DF and PWHT current schedule led to the deviation of cracks at the edge of the FZ. In contrast, the crack propagated directly into the FZ along the columnar structure in the as-welded condition. The novel application of in-situ DF extends beyond the conventional PWHT and offers a promising avenue to trigger microstructural changes in the weld which can improve mechanical performance and overall crashworthiness.

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 score0.046
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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