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On the Cyclic Deformation Response and Microstructural Mechanisms of ECAPed and ARBed Copper - an Overview

2011· article· en· W1998756749 on OpenAlexaff
Charles C.F. Kwan, Zhi Rui Wang

Bibliographic record

VenueMaterials science forum · 2011
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceCopperSevere plastic deformationDeformation (meteorology)MetallurgyAccumulative roll bondingMicrostructurePressingComposite material

Abstract

fetched live from OpenAlex

With the increase of interest in using ultra-fine and nano-grained metals for structural purposes, the need to build on the knowledge pool regarding the response and behaviour of those metals under a mechanical load becomes more vital. However, it is well known that, especially for this type of materials such as the ECAPed and ARBed materials, the thermo-mechanical history affects the mechanical behaviour of the product strongly. Although ECAP and ARB are different techniques under the category of severe plastic deformation, similarities in their cyclic deformation response is observed from time to time. Specifically, the microstructural mechanisms involved in accommodating cyclic plastic strain in these two types of materials is seemingly comparable. The similarities arise from the similar microstructures in the majority of the volume of the bulk. In this report, the cyclic deformation response, and the related microstructural mechanisms of ECAPed copper will be discussed first and those of ARBed second. A comparison between ECAPed copper and ARBed copper will then be performed. Furthermore, the differences due to the unique features of ARBed material will be discussed. Lastly, the reasons behind the observed similarities in cyclic deformation behaviour and the related micro-mechanisms for metals process with the two different techniques will also be explored.

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)0.002
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 score0.004
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.039
GPT teacher head0.259
Teacher spread0.220 · 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
Published2011
Admission routes1
Has abstractyes

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