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Record W4200143499 · doi:10.1177/16878140211070446

Finite element modeling of sub-surface damage while machining aluminum based metal matrix composites

2021· article· en· W4200143499 on OpenAlexaff
Usama Umer, Hisham Alkhalefah, Mustufa Haider Abidi, Muneer Khan Mohammed, Hossam A. Kishawy

Bibliographic record

VenueAdvances in Mechanical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsOntario Tech University
FundersKing Abdulaziz City for Science and Technology
KeywordsMaterials scienceComposite materialMachiningBrittlenessFracture (geology)Particle (ecology)Finite element methodCeramicAluminiumMatrix (chemical analysis)Metal matrix compositeStructural engineeringMetallurgy

Abstract

fetched live from OpenAlex

Sub-surface damage during machining of aluminum-based metal matrix composites (MMCs) has been modeled using finite element models. These models are based on reinforcement particles size and volume fractions and particles are distributed uniformly in the metal matrix. In order to simulate particle debonding cohesive zone elements (CZE) have been incorporated along the parting line. In addition, failure criteria based on brittle fracture have been added for ceramic particles to simulate particle fracture. To reduce computational time and simplify the model both CZE and particle fracture is limited to the reinforced particles along the parting lines facing the tip of the cutting tool. The damage depth beneath the machined surface is measured by using the non-zero plastic strain values in the equivalent plastic strain contours obtained from the FE models. The results were compared against published experimental data and found to be in good agreement.

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 categoriesMeta-epidemiology (narrow)
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 score0.581
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.223
Teacher spread0.212 · 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.

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

Citations7
Published2021
Admission routes1
Has abstractyes

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