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Record W2809865809 · doi:10.26776/ijemm.03.02.2018.04

Parametric Effect on Surface Finish of Three-Dimensional Printed Object

2018· article· en· W2809865809 on OpenAlexaff
Muhammad Hasibul Hasan, Shugata Ahmed, Ron Voldman, Mirt Mehany

Bibliographic record

VenueInternational Journal of Engineering Materials and Manufacture · 2018
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsOvalitySurface roughnessMaterials scienceSurface finishFused deposition modelingResolution (logic)Surface (topology)AcetoneDeposition (geology)Parametric statistics3D printingComposite materialGeometryComputer scienceMechanical engineeringMathematicsChemistryEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The ability to use different parameters and finishing techniques in fused deposition modelling (FDM) depends largely on part geometries, materials, printing processes like Z-resolution and post processing to some extent. Low quality poor surface finish due to layer ovality, improper Z-resolution parameter selection and fill of the empty shell in three-dimensional (3D) printing, results unexpected texture and appearance. An investigation is carried out on the effects of Z-resolution (0.15 mm to 0.40 mm) and Fill parameters on flat and curved surface objects manufactured using the FDM process. Moreover, post surface treatment was performed using acetone. It was found that average surface roughness increases with increasing Z-resolution. Solid Fill can create a smoother surface as oppose to Sparse Fill. Surface roughness improves significantly after post treatment with organic solvent acetone.

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 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 score0.241
Threshold uncertainty score0.599

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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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