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Record W2885710519 · doi:10.1002/adem.201800312

Intermetallic Compounds in Al‐SUS316L Composites

2018· article· en· W2885710519 on OpenAlexaff
Kwang-Jae Park, Dasom Kim, Jehong Park, Seungchan Cho, Takamichi Miyazaki, Hansang Kwon

Bibliographic record

VenueAdvanced Engineering Materials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsNexen (Canada)
FundersPukyong National University
KeywordsIntermetallicMaterials scienceSpark plasma sinteringComposite materialElectron microprobeScanning electron microscopeThermogravimetric analysisComposite numberSinteringMetallurgyAlloyChemical engineering

Abstract

fetched live from OpenAlex

Aluminum (Al)‐stainless steel 316L (SUS316L) composites are successfully manufactured by spark plasma sintering (SPS) using pure Al and SUS316L powders as the raw materials. The Al‐SUS316L composite powder is prepared from a 1:1 mixture by volume of Al and SUS316L by mechanical ball milling. This composite powder is subjected to SPS at four different temperatures (500, 550, 600, and 630 °C) at a pressure of 200 MPa and held at the desired temperature for 5 min. Intermetallic compounds such as AlFe3 and Al13Fe4, which are detected based on their X‐ray diffraction (XRD) patterns, are created in the Al‐SUS316L composites during SPS. In addition, scanning electron microscopy (SEM), energy dispersive X‐ray spectroscopy (EDS), field emission‐electron probe microanalysis (FE‐EPMA), and thermogravimetric and differential thermal analysis (TG‐DTA) also confirm the formation of intermetallic compounds. Moreover, the authors conduct a detailed analysis of the intermetallic compounds using transmission electron microscopy (TEM). The intermetallic compounds are well dispersed in Al‐SUS316L composites and the layer of these becomes thicker as the sintering temperature increases to 630 °C. Moreover, the intermetallic compounds can help to make a strong chemical bonding between Al and SUS316L matrix. Consequently, the Al‐SUS316L composites manufactured by the SPS process can be applied in engineering industries such as the automobile, aerospace, and construction industries as high‐strength and lightweight materials.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.232
Teacher spread0.226 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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