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Record W4211019035 · doi:10.5539/jmsr.v1n1p54

Study on Polishing DF2 (AISI O1) Steel by Nd: YAG Laser

2011· article· en· W4211019035 on OpenAlexvenueno aff
Kelvii Wei Guo, Hon-Yuen Tam

Bibliographic record

VenueJournal of Materials Science Research · 2011
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsnot available
FundersCity University of Hong Kong
KeywordsPolishingMaterials scienceLaserProfilometerScanning electron microscopeOpticsIrradiationOptical microscopeEvaporationComposite materialSurface roughness

Abstract

fetched live from OpenAlex

Pulse Nd: YAG laser was used to polish DF2 cold work steel. Influence of irradiation parameters on the 3D surface topography was studied by 3D profilometer, scanning electron microscopy (SEM), and atomic force microscope (AFM). Results among the tests showed when DF2 specimens were irradiated with parameters of (i) laser input energy P=1 J, (ii) pulse feedrate=300 mm/min, (iii) pulse duration PD=3 ms, and (iv) pulse frequency f=20~25 Hz, laser polishing of DF2 cold work steel seemed to be successful. Also, effect of laser polished parameters on the laser polishing temperature was described and the analysis of temperature field of laser polishing was herewith proposed. It demonstrated that for a given laser, influence of laser pulse feedrate was more prominent than that of other parameters on the topography of a laser polished surface. Moreover, the mechanism of laser polishing relied on the heat-input interaction with the base metal. When Hinput=Hthreshold, the mechanism of laser polishing principally relied on the metal melting; When Hinput>Hthreshold, the mechanism of laser polishing was a combination of metal evaporation and melting; and when Hinput>>Hthreshold, the mechanism of laser polishing mainly relied on the metal evaporation.

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)0.010
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 score0.010
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.137
GPT teacher head0.386
Teacher spread0.250 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2011
Admission routes1
Has abstractyes

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