MétaCan
Menu
Back to cohort
Record W3096495639 · doi:10.1139/tcsme-2020-0137

Experimental investigation of mechanical and tribological properties of Al6061–ZrC–B<sub>4</sub>C hybrid composites

2020· article· en· W3096495639 on OpenAlexvenueno aff
Theerkka Tharaisanan Rajamanickam, K. Marimuthu

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceComposite materialUltimate tensile strengthBoron carbideThermogravimetric analysisTribologyScanning electron microscopeIzod impact strength testThermal stabilityZirconium carbideAluminiumCarbide

Abstract

fetched live from OpenAlex

Aluminium metal matrix composites (ALMMCs) have been widely used because of their superior properties such as high strength to wear ratio, high wear resistance, and higher heat conduction rate. The addition of reinforcements in the form of discontinuous particles leads to an increase in the properties of metal matrix composites (MMCs). In the present study, ALMMCs were fabricated with the addition of discontinuous reinforcement particles of zirconium carbide (ZrC) and boron carbide (B 4 C). The mechanical properties such as tensile strength, hardness, and impact strength were tested as per ASTM standards. The tribological properties were tested using a pin-on-disc setup under different loading conditions (10, 20, 30, 40 N). Moreover, the morphological characterisation of the ALMMCs was carried out by scanning electron microscope (SEM) analysis. Furthermore, differential thermal analysis (DTA) and thermogravimetric analysis (TGA) were performed to find the thermal stability of the ALMMCs. The findings show that variations of reinforcement of ZrC added gave improved properties such as hardness, tensile strength, impact strength, and wear resistance.

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 score0.241
Threshold uncertainty score0.636

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 head0.020
GPT teacher head0.176
Teacher spread0.156 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicAluminum Alloys Composites PropertiesFrench-language works237,207