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Record W3032346079 · doi:10.1504/ijmpt.2019.10024476

Mechanical capabilities of semi-rigid thermoplastics ABS and PLA from 3D printing

2019· article· en· W3032346079 on OpenAlexaff
Gustavo Adolfo Moreno Jiménez, Santiago Gómez Rosero, Diana Peralta-Zurita, Jaime Vinicio Molina Osejos, César Ayabaca

Bibliographic record

VenueInternational Journal of Materials and Product Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials scienceUltimate tensile strengthAcrylonitrile butadiene styreneComposite materialPolylactic acidBendingShore durometerYoung's modulus3D printingTensile testingPolymer

Abstract

fetched live from OpenAlex

The use of 3D printing is more common in industries, such as textiles, food, aeronautics, automotive, medicine and others, because of its great versatility during the manufacturing process generation of elements or components with high geometric complexity in relatively short time at low cost. This study presents the characterisation of semi-rigid acrylonitrile-butadiene-styrene (ABS) and polylactic acid (PLA), which was obtained using both of additive manufacturing and specifically the molten material deposition method. The semi-rigid samples were made with five different infill patterns in their interiors: triangle, square, pentagon, and circles. The samples were subjected to tensile and bending tests, which allowed the evaluation of the yield strength, ultimate tensile strength, deformation and modulus of elasticity. Also, ASTM D638-14 and ASTM D790-10 norms were used for methodology validation. The results determined that the distribution of concentric circles with 7,964 MPa/g and 12,234 MPa/g was the best for PLA and the square with 8.82 MPa/g and 8,994 MPa/g was the best for ABS respectively.

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 score0.025
Threshold uncertainty score0.344

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 head0.006
GPT teacher head0.208
Teacher spread0.201 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

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