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Record W2942580786 · doi:10.1126/sciadv.aav5790

Grayscale digital light processing 3D printing for highly functionally graded materials

2019· article· en· W2942580786 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

VenueScience Advances · 2019
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsSurrey Memorial Hospital
FundersAir Force Office of Scientific ResearchNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsGrayscaleDigital Light ProcessingComputer scienceComputer graphics (images)Digital printing3D printingComputer visionMaterials scienceArtificial intelligenceEngineering drawingComposite materialEngineeringImage (mathematics)

Abstract

fetched live from OpenAlex

Three-dimensional (3D) printing or additive manufacturing, as a revolutionary technology for future advanced manufacturing, usually prints parts with poor control of complex gradients for functional applications. We present a single-vat grayscale digital light processing (g-DLP) 3D printing method using grayscale light patterns and a two-stage curing ink to obtain functionally graded materials with the mechanical gradient up to three orders of magnitude and high resolution. To demonstrate the g-DLP, we show the direct fabrication of complex 2D/3D lattices with controlled buckling and deformation sequence, negative Poisson's ratio metamaterial, presurgical models with stiffness variations, composites for 4D printing, and anti-counterfeiting 3D printing.

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 score0.390
Threshold uncertainty score0.453

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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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