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Record W4403063212 · doi:10.1016/j.jmrt.2024.10.003

Design of explosively welded Fe–Al multilayer laminated composite pipes: A critical microscopy analysis of stand-off distance and post-weld heat treatment effects on interface properties

2024· article· en· W4403063212 on OpenAlexaff
Mohammad Reza Jandaghi, Hesam Pouraliakbar, Johan Moverare, Vahid Fallah, Gholamreza Khalaj

Bibliographic record

VenueJournal of Materials Research and Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsMaterials scienceWeldingComposite numberComposite materialExplosion weldingMicroscopyInterface (matter)MetallurgyOpticsFiller metal

Abstract

fetched live from OpenAlex

This study examined the microstructure and mechanical properties of explosively welded (EXW) Fe–Al multilayer laminated composite pipes, specifically SS321/AA1050/AA5083, with varying stand-off distances (SD) and post-weld heat treatments (PWHT). Findings revealed that higher collision forces at higher-SD produced a wavy interface with finer grains at the AA1050/AA5083 interface. In contrast, lower-SD yielded a smooth, flat interface. At the SS321/AA1050 interface, collision energy resulted in the formation of an uneven composite reaction layer comprising multiple intermetallics. Analysis of the SS321/AA1050 interface revealed that an increase in the PWHT temperature results in diffusion toward the reaction layer, transforming the unevenly Al-rich and Fe-rich phases into a uniform Al 85 (Fe,Cr,Ni) 15 solid solution. The increase in the fraction of this phase made the reaction layer more brittle. At the AA1050/AA5083 interface, PWHT at higher temperatures caused a significant hardness decline extending further from the interface in samples with higher-SD. On the SS321 side of the laminated composite, higher SD resulted in higher kernel average misorientation (KAM) and increased hardness. Despite a decrease in hardness after PWHT at 350 °C, the hardness gradient from the matrix to the interface in laminates with higher SD remained relatively unaffected, whereas the lower SD counterparts experienced a noticeable stress relief.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.033
GPT teacher head0.352
Teacher spread0.319 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2024
Admission routes1
Has abstractyes

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