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Record W7082659754 · doi:10.1007/s11665-025-11999-5

Comprehensive Evaluation of Double-Wire Narrow Gap GMAW Process and Dissimilar Joint for Chute Structure

2025· article· en· W7082659754 on OpenAlexaff

Bibliographic record

VenueJournal of Materials Engineering and Performance · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsSheridan College
Fundersnot available
KeywordsWeldingGas metal arc weldingHeat-affected zoneMicrostructureJoint (building)Filler metalMartensiteElectric resistance weldingArc welding

Abstract

fetched live from OpenAlex

Abstract Chute structures are critical components for transporting mined coal in coal machinery, and their manufacturing involves welding dissimilar thick-plate materials. To achieve efficient and high-quality fabrication of chute structures, double-wire narrow gap gas metal arc welding (GMAW) was utilized to join 40-mm-thick wear-resistant steel NM450 and cast steel ZG30SiMn. A comprehensive evaluation was conducted on the welding process, microstructure, and mechanical properties of the welded joint. Additionally, temperature field simulations were performed to investigate the impact of multi-layer welding thermal cycles on microstructural evolution. The double-wire narrow gap GMAW process demonstrated high stability, yielding a defect-free welded joint. The weld metal (WM) microstructure comprised proeutectoid ferrite, polygonal ferrite, and acicular ferrite, which endowed the WM with better ductility and toughness. Due to the welding-induced multiple thermal cycles, a white band formed between filling layers. In the heat-affected zone (HAZ), the microstructure consisted of martensite and bainite, achieving higher strength and 86-110% toughness values of the base metals. This study successfully implemented double-wire narrow gap GMAW for chute fabrication, enabling high-quality welding of thick-plate dissimilar joint while enhancing welding efficiency without compromising performance. The findings provide actionable insights for optimizing chute manufacturing in heavy machinery industries.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.026
GPT teacher head0.260
Teacher spread0.234 · 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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