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Record W2886482167 · doi:10.1016/j.promfg.2018.07.040

Vibration-assisted dimple generation on bulk metallic glass

2018· article· en· W2886482167 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

VenueProcedia Manufacturing · 2018
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDimpleMaterials scienceAmorphous metalMicrostructureGroove (engineering)MachiningDeformation (meteorology)MetallurgyChip formationTexture (cosmology)Composite materialAmorphous solidTool wearVibrationAlloy

Abstract

fetched live from OpenAlex

Bulk metallic glasses (BMGs) are metallic materials without crystalline microstructure. The mechanism of chip formation in machining BMGs is different from crystalline metal alloys due to their amorphous phase and mechanical properties. This paper presents experimental investigations on dimple generation of Zr- BMG. The inclined high feed milling process is used to generate dimples on the surface with single flute PCD tools. To understand the deformation characteristics and process performance, chip formation, surface texture and tool wear in texturing BMG are compared with OFHC (oxygen free high thermal conductivity) copper and AISI 304 stainless steel. The effect of vibration assistance on the process performance and material deformation is analyzed. The results show that the quality of texture generated in vibration-assisted dimple generation is more uniform compared to conventional inclined milling.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.884

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 head0.027
GPT teacher head0.230
Teacher spread0.203 · 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