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Record W2979826118 · doi:10.1115/1.4045129

Effects of Ultrasonic Vibration Assistance on Chip Formation Mechanism in Cutting of Ti–6Al–4V

2019· article· en· W2979826118 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Manufacturing Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceChipVibrationChip formationShear (geology)Shear stressUltrasonic sensorComposite materialFinite element methodMechanism (biology)Structural engineeringAcousticsEngineeringMetallurgyMachiningTool wear

Abstract

fetched live from OpenAlex

Abstract This paper presents the effects of ultrasonic vibration assistance on shear band formation and chip segmentation mechanism in orthogonal cutting of Ti–6Al–4V. Experimental observations of chip microstructure show that the shear bands disappear when vibration assistance is applied along tangential direction at certain cutting speeds. A plastic chip flow model is developed to predict the stress and temperature variations in the primary shear zone at cutting, chip elastic recovery, and tool-chip separation periods. The simulation results show that the temperature in the primary shear zone in vibration-assisted cutting is much lower when compared with conventional cutting, therefore suppresses the generation of shear bands. The simulations of average cutting forces and pitch lengths of chip segments are compared with the experimental results. A finite element model is further developed to prove the temperature reduction when ultrasonic vibration assistance is applied.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.308

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 communication0.0000.001
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 head0.003
GPT teacher head0.186
Teacher spread0.184 · 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