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Record W3128817180 · doi:10.1115/1.4049965

Development and Optimization of Ultrasonic Elliptical Vibration Cutting Device Based on Single Excitation

2021· article· en· W3128817180 on OpenAlex

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

VenueJournal of Manufacturing Science and Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Machining and Optimization Techniques
Canadian institutionsUniversity of British Columbia
FundersScience Challenge ProjectNational Science and Technology Planning Project
KeywordsVibrationDiamond turningFinite element methodFrench hornMaterials scienceExcitationUltrasonic sensorAcousticsTungstenMechanical engineeringStructural engineeringDiamondEngineeringMetallurgyPhysics

Abstract

fetched live from OpenAlex

Abstract A ultrasonic elliptical vibration cutting (UEVC) technique, as an advanced cutting method, has been successfully applied to machine difficult-to-cut materials for the last decade. In this study, the mechanism of the elliptical vibration locus caused by the “asymmetric structure” on the horn was analyzed theoretically first, and the corresponding relationship between the degree of asymmetry and the elliptical vibration locus was determined based on the finite element method (FEM). Then, an efficient single-excitation UEVC device with “asymmetric structure” was developed and optimized. The resonant frequency of the device was 40.8 kHz, and the amplitude reached 14 µm, which effectively broke the limitation of cutting speed in UEVC. Finally, the UEVC device’s performance was tested, and the advantages in improving the tungsten alloy surface quality and reducing diamond cutting tool wear validated the technical capability and principle of the proposed device.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.426
Threshold uncertainty score0.314

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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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