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Record W2900035382 · doi:10.1002/pen.24955

Improved part strength for the fused deposition 3D printing technique by chemical modification of polylactic acid

2018· article· en· W2900035382 on OpenAlexafffund
Mohammed T. Alturkestany, Vishrut Panchal, Michael R. Thompson

Bibliographic record

VenuePolymer Engineering and Science · 2018
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaSaudi Arabian Cultural Bureau
KeywordsPolylactic acidMaterials scienceUltimate tensile strengthComposite materialBond strengthRheology3D printingFused deposition modelingBranching (polymer chemistry)PolymerAdhesionLayer (electronics)Adhesive

Abstract

fetched live from OpenAlex

Fused deposition method (FDM) is popular as a plastic 3D printing technique. One of the drawbacks of this technology is its low bonding strength between layers, reducing through‐plane mechanical properties of a part compared with in‐plane strength within the layers themselves. This study focuses on altering the molecular structure of a polylactic acid by chain extension to increase the adhesion strength between layers, quantified by peel testing, in order to increase overall part strength. Four different samples were prepared in a high shear mixer, processed into filament and printed by a FDM‐type 3D printer. These samples were characterized for their thermal, rheological, mechanical and adhesion properties. The findings showed that the chain extender‐modified resins exhibited higher layer‐to‐layer adhesion strength as well as increased viscosity corresponding to an increasing degree of branching. The interlayer diffusion and entanglement of newly created branch chain ends improved bonding between the printed layers resulting in higher tensile properties. POLYM. ENG. SCI., 59:E59–E64, 2019. © 2018 Society of Plastics Engineers

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 score0.444
Threshold uncertainty score0.350

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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

Citations15
Published2018
Admission routes2
Has abstractyes

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