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Analysis of the reciprocal wear testing of Aluminum AA1050 processed by a novel mechanical nanostructuring technique

2021· article· en· W3164694683 on OpenAlexaff
Babak Omranpour Shahreza, Lembit Kommel, Fjodor Sergejev, Julia Ivanisenko, Maksim Antonov, M.A.L. Hernández-Rodríguez, E. García-Sánchez

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersEesti Teadusagentuur
KeywordsMaterials scienceIndentation hardnessSevere plastic deformationShearing (physics)Electron backscatter diffractionExtrusionTorsion (gastropod)PlasticityComposite materialWear resistanceAluminiumMetallurgyMicrostructure

Abstract

fetched live from OpenAlex

Abstract This research aims to investigate the impact of a novel technique in mechanical nanostructuring on the wear resistance of materials. This technique with the name of High Pressure Torsion Extrusion (HPTE) can produce bulk nanostructured materials with enhanced mechanical properties. Results of microstructural analysis and microhardness testing showed significant enhancement in materials after HPTE. Microstructural characterization by using Electron Back-Scattered Diffraction (EBSD) method illustrated the presence of Ultra-Fine Grained (UFG) materials in the specimens Analysis of the wear by implementing reciprocal wear testing revealed that the amount of displaced volume markedly decreased after processing. This change in the wear behavior can be explained by referring to the hardness increase and the reduction of plasticity in materials which confined the plastic shearing and diminished the built-up edge around the wear track.

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)0.001
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 score0.005
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.016
GPT teacher head0.210
Teacher spread0.194 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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