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Record W2322755478 · doi:10.1115/imece2004-59445

A Model for Orthogonal Machining of Metal Matrix Composite Using Finite Element Method

2004· article· en· W2322755478 on OpenAlexaff
Yao Peng Zhu, Sathish Kannan, Hossam A. Kishawy

Bibliographic record

VenueApplied Mechanics · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsFinite element methodAluminiumMaterials scienceMachiningMetal matrix compositeComposite numberComposite materialMatrix (chemical analysis)Aluminium oxideStress (linguistics)Structural engineeringMetallurgyEngineering

Abstract

fetched live from OpenAlex

In this paper, a thermo-elasto plastic finite element model for machining of aluminium based particulate composite is presented. The presented model accounted for the thermoelasto-plastic properties of the material under consideration and explicit finite element solution was used. The aluminium oxide particles were modeled as perfectly elastic and the shapes considered were irregular. The interface between the aluminium oxide particulate and aluminium matrix was incorporated in this model. The effective and the shear stress profiles on the aluminium oxide particles were analyzed. The simulation values of cutting and feed forces were compared with the experimental data and were found to be well within the limits of divergence, thus, validating the proposed finite element model.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.291
Teacher spread0.268 · 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 designSimulation or modeling
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
Published2004
Admission routes1
Has abstractyes

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