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Record W4210454618 · doi:10.1115/imece2021-69667

Tool Path Generation for Free Form Surface Slicing In Additive Manufacturing/Fused Filament Fabrication

2021· article· en· W4210454618 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

VenueVolume 2B: Advanced Manufacturing · 2021
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsYork University
Fundersnot available
KeywordsSlicingFused filament fabricationCasing3D printingPlanarFabricationComputer scienceEngineering drawingSurface (topology)Program slicingStack (abstract data type)Mechanical engineeringMaterials scienceComputer graphics (images)EngineeringGeometryMathematics

Abstract

fetched live from OpenAlex

Abstract This study is dedicated to develop 3D printing slicing for more specialised applications. Creation of a physical model is a product of the slicing procedure which results in a stack of 2D planar images. Effects of planar slicing results in stair casing effect and is known to effect the mechanical integrity of the fabricated part. To mitigate such effects we present a preliminary study on free form surface slicing procedure. Surface slicing can compliment current 3D printing to develop more customized solutions in fabrication. Results presented show that the end product yielded is superior than its planar slicing counterpart in two aspects. Firstly, stair casing effect can be avoided and secondly visibly greater degree of visible aesthetics can be achieved. Surface slicing is performed on rectangular patch type geometry. Finally, outcomes of this study has been discussed for further development of surface slicing for more customised applications.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
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 head0.013
GPT teacher head0.220
Teacher spread0.207 · 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