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Fixture Design Based on Magneto-Rheological Fluids for Thin Wall Spherical Shell Precision Machining

2011· article· en· W2053093264 on OpenAlex
Jin Xing Kong, Yong Cheng Zheng, Xixi Wang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced materials research · 2011
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsCanadian Association of Emergency Physicians
Fundersnot available
KeywordsFixtureMaterials scienceStiffnessMachiningShell (structure)Rigidity (electromagnetism)Magnetic fieldMagnetoRheologyProcess (computing)Spherical shellMechanical engineeringMagnetComposite materialComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Thin wall spherical shell is easy to deform at the process of turning because the low rigidity. In order to improve process precision of the thin wall spherical shell, the thought increasing the workpiece technology stiffness is put forward, which the magneto-rheological fluids (MRF) is filled inside the thin wall spherical shells as strengthening phase transition material. In magnetic field, MRF can transform from fluid to solid and solid back to fluid rapidly. According to the thought, the fixture designed is applicable for the thin wall spherical shells process precision. The clamp stiffness can be controlled and the MRF can be used many times. In order to get uniform intensity of the magnetic field in MRF, the special magnetic device is designed which is based on the variable MRF thickness and the magnetic field distributing is analyzed and optimized. The fixture based on MRF will help to improve machining precision of the thin wall spherical shells.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0030.000

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 head0.091
GPT teacher head0.318
Teacher spread0.227 · 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