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Record W4394691853 · doi:10.1139/tcsme-2023-0148

Milling characteristics of SiCp/2024Al composites thin-walled part based on movable auxiliary support

2024· article· en· W4394691853 on OpenAlexvenueno aff
Zhijie Gao, Peng Zhu, Shu Yang, Zuoshan Wei, Qitong Wang

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceComposite materialComposite number

Abstract

fetched live from OpenAlex

Due to milling force and milling heat, thin-walled parts are highly susceptible to deformation and even scrap. In this paper, a method of milling thin-walled part based on the movable auxiliary support was proposed. The ABAQUS software was employed to simulate the milling of thin-walled SiCp/2024Al composite part, while the movement of the auxiliary support was simulated using the secondary development subroutine VDLOAD. The influences of the magnitude and area of the movable auxiliary support on the surface temperature, deformation, and residual stress of the workpiece were investigated by single factor method. The results showed that, with the increasing of the magnitude and area of the movable auxiliary support, both surface temperature and deformation of the thin-walled part decrease. The tensile residual stress decreases and gradually changes to compressive residual stress. Interestingly, when the magnitude exceeds 20 MPa or the area exceeds 100 mm 2 , the deformation of the thin-walled part continues to decrease, but the middle of the thin-walled part is concave toward the milling cutter. It is worth noting that the effect of area on the residual stress does not show a clear regularity. Thus, the reasonable auxiliary support can reduce deformation and stability in the milling of thin-walled parts and the results of the study can provide a theoretical basis for optimizing the milling process of thin-walled parts.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.547

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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

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