MétaCan
Menu
Back to cohort
Record W2943103049 · doi:10.1115/1.4043672

Comparing Slicing Technologies for Digital Light Processing Printing

2019· article· en· W2943103049 on OpenAlexafffund
Tsz-Ho Kwok

Bibliographic record

VenueJournal of Computing and Information Science in Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSlicing3D printingProcess (computing)Digital Light ProcessingComputer scienceEngineering drawingTracingCADDigital manufacturingDigital printingComputer graphics (images)EngineeringManufacturing engineeringMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In additive manufacturing (AM), slicing is a crucial step in process planning to convert a computer-aided design (CAD) model to a machine-specific format. Digital light processing (DLP) printing is an important AM process that has a good surface finish, high accuracy, and fabrication speed and is widely applied in many dental and engineering industries. However, as DLP uses images for fabrication different from other toolpath-based processes, its process planning is understudied. Therefore, the main goal of this paper is to study and compare the slicing technologies for DLP printing. Three slicing technologies are compared: contour, voxelization, and ray-tracing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Computing and Information Science in EngineeringSame topicAdditive Manufacturing and 3D Printing TechnologiesFrench-language works237,207