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Record W2597374800 · doi:10.1108/rpj-10-2015-0151

Tensile strength of partially filled FFF printed parts: meta modelling

2017· article· en· W2597374800 on OpenAlexaff
Shahrain bin Mahmood, Ahmed Jawad Qureshi, Kheng Lim Goh, Didier Talamona

Bibliographic record

VenueRapid Prototyping Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsUltimate tensile strengthMaterials scienceParametric statisticsFused filament fabricationCross section (physics)Composite materialRoofStructural engineering3D printingMathematicsEngineering

Abstract

fetched live from OpenAlex

Purpose This paper aims to investigate the tensile strength of partially filled fused filament fabrication (FFF) printed parts with respect of cross-sectional geometry of partially filled test pieces. It was reported in the authors’ earlier work that the ultimate tensile strength (UTS) is inversely proportional to the cross-sectional area of a specimen, whereas the number of shells and infill density are directly proportional to the UTS with all other parameters being held constant. Here, the authors present an in-depth evaluation of the phenomenon and a parametric model that can provide useful estimates of the UTS of the printed part by accounting for the dimensions of the solid floor/roof layers, shells and infills. Design/methodology/approach It was found that partially filled FFF printed parts consist of hollow sections. Because of these voids, the conventional method of determining the UTS via the gross cross-sectional area given by A = b × h, where b and h are the width and thickness of the printed part, respectively, cannot be used. A mathematical model of a more accurate representation of the cross-sectional area of a partially filled part was formulated. Additionally, the model was extended to predict the dimensions as well as the lateral distortion of the respective features within a printed part using input values from the experimental data. Findings The result from this investigation shows that to calculate the UTS of a partially filled FFF part, the calculation based on the conventional approach is not sufficient. A new meta-model is proposed which takes into account the geometry of the internal features to give an estimate of the strength of a partially filled printed part that is closer to the value of the strength of the material that is used for fabricating the part. Originality/value This paper investigates the tensile strength of a partially filled FFF printed part. The results have shown that the tensile strength of a partially filled part can be similar to that of a solid part, at a lower cost: shorter printing time and lower material usage. By taking into account the geometries within a printed part, the cross-sectional area can be accurately represented. The mathematical model which was developed would aid end-users to predict the tensile strength for a given set of input values of the process parameters.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.257
Teacher spread0.206 · 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 designSimulation or modeling
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

Citations18
Published2017
Admission routes1
Has abstractyes

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