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Record W4313253981 · doi:10.6000/2369-3355.2022.09.05

Surface Microprocessing AISI-O1 by Pulsed Nd: YAG Laser

2022· article· en· W4313253981 on OpenAlexvenueno aff
Kelvii Wei Guo

Bibliographic record

VenueJournal of Coating Science and Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceLaserMoldScanning electron microscopeProfilometerIrradiationOptical microscopePulse durationMorphology (biology)Nd:YAG laserDie (integrated circuit)Composite materialMetallurgyOpticsNanotechnologySurface finish

Abstract

fetched live from OpenAlex

Due to tedious and time-consuming work put into the conventional mold/die microprocessing (about 37% of the total time of the entire mold/die production). Therefore, to improve or enhance the quality of mold/dies at the final step with a less number of trained and skilled operators or decrease the processing time to cut the overall cost dramatically, AISI-O1 cold work steel was micro-processed by Pulsed Nd: YAG laser. The influence of laser processing parameters on the evolution of the correlated surface morphology was investigated by a 3D profilometer, scanning electron microscopy (SEM), and optical microscopy (OM). The results show that when AISI-O1 specimens were irradiated with various parameters, the morphology of AISI-O1 cold work steel was changed correspondingly. It also demonstrates that the different kinds of micro-processed surface morphology could be established successfully to satisfy the later practical requirements for a given laser. Meanwhile, the effect of laser processing parameters on the relevant temperature was described, and the laser microprocessing temperature field was also proposed. It reveals that the influence of laser pulse feed rate was more prominent than that of other parameters on the relevant micro-processed surface morphology.

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)0.001
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 score0.005
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.005
GPT teacher head0.224
Teacher spread0.219 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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