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Record W4388903740 · doi:10.6000/1929-5995.2023.12.15

The Effect of AZ61 Content on Mechanical Strength and Surface Hardness of PA6-AZ61 Magnesium Alloy

2023· article· en· W4388903740 on OpenAlexvenueno aff
Yopi Yusuf Tanoto, Song‐Jeng Huang

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2023
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsnot available
FundersNational Science and Technology Council
KeywordsMaterials scienceUltimate tensile strengthIzod impact strength testScanning electron microscopeIndentation hardnessComposite materialAlloyCompression moldingComposite numberPolyamideMagnesium alloyMetallurgyMicrostructure

Abstract

fetched live from OpenAlex

In this study, a Polyamide 6 (PA6)-AZ61 magnesium alloy composite and pure PA6 were fabricated using a compression molding instrument. Both the matrix and reinforcement were prepared in powder form. A planetary ball milling machine was employed to mix the PA6 and AZ61 micro powders. The effects of AZ61 content at different percentage on the final properties of the composite were investigated. X-ray diffraction (XRD) analysis and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) were employed to verify the uniformity of the mixing process and to confirm the composition of both the raw materials and the composite. The result, relative to pristine PA6, the ultimate tensile strength (UTS) demonstrated a substantial increment of 48.3%, reaching 58 MPa. Whereas the yield strength (YS) exhibited a notable surge to 49.38 MPa, constituting a 52.9% enhancement. Additionally, the PA6-5AZ61 composition achieved the highest microhardness value at 21.162 HV, signifying a remarkable 66.3% augmentation compared to the unalloyed PA6 material. This result suggests that AZ61 has the potential to improve the properties of the matrix material.

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)0.007
metaresearch head score (Gemma)0.001
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 score0.017
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.317
Teacher spread0.280 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Research Updates in Polymer ScienceSame topicAluminum Alloys Composites PropertiesFrench-language works237,207