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Record W4387801273 · doi:10.1145/3586183.3606718

Substiports: User-Inserted Ad Hoc Objects as Reusable Structural Support for Unmodified FDM 3D Printers

2023· article· en· W4387801273 on OpenAlexafffund
Ludwig Wall, Oliver Schneider, Daniel Vogel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsSlicingComputer scienceSoftwareEngineering drawingFused deposition modeling3D printingHuman–computer interactionComputer graphics (images)Operating systemEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

We contribute a technical solution to reduce print time and material with unmodified fused deposition modelling printers. The approach uses ad hoc objects inserted by a user during printing as a replacement for printed support of overhanging structures. Examples of objects include household items like books, toy bricks, and custom mechanisms like a screw jack. A software-only system is integrated into existing slicing software to analyze generated support print paths, search a library of objects to find suitable replacements, optimize combinations of replacement objects, and make necessary adjustments to impacted printing layers and paths. During printing, the user is prompted to insert objects with the help of lightweight printed holders to guide placement and prevent movement. Instructions printed on the build-plate help identify and position objects. A technical evaluation measures performance and benefits with different sets of ad hoc objects and different levels of user involvement.

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)0.000
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 score0.734
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.019
GPT teacher head0.248
Teacher spread0.229 · 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.

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
Published2023
Admission routes2
Has abstractyes

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