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Record W4409530888 · doi:10.1002/pen.27224

Investigating the impact of <scp>3D</scp> printing process parameters on the mechanical and morphological properties of fiber‐reinforced thermoplastic polyurethane composites

2025· article· en· W4409530888 on OpenAlexaff
Sabrina Islam, Z. Shaghayegh Bagheri

Bibliographic record

VenuePolymer Engineering and Science · 2025
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsToronto Rehabilitation InstituteUniversity Health Network
FundersVirginia Innovation Partnership Corporation
KeywordsMaterials scienceComposite materialThermoplastic polyurethaneFiberPolyurethaneThermoplastic compositesThermoplastic

Abstract

fetched live from OpenAlex

Abstract Additive manufacturing (AM) is groundbreaking technology that has gained attention for minimizing material waste and enabling tool‐free production of multi‐material structures. This study examines the effect of process parameters on the mechanical properties of patent‐pending composites comprising thermoplastic polyurethane, hexagonal‐boron‐nitride, carbon, and zylon fiber, designed for durable ice friction in footwear outsoles, produced using Fused Filament Fabrication (FFF), a widely adopted AM technology. Experiments are designed using the Taguchi method, followed by analysis of variance (ANOVA) to identify parameters with the most significant influence. Parameters include filament extrusion temperature, platform temperature, layer height, printing speed, and printing orientation. Extrusion temperature, layer height, and orientation significantly influenced elastic modulus and modulus of resilience (extrusion temperature: p = 0.012, contribution = 12.12% &amp; p = 0.019, contribution = 23.49%; layer height: p &lt; 0.001, contribution = 34.33% &amp; p = 0.008, contribution = 34.63%; orientation: p = 0.001, contribution = 46.11% &amp; p = 0.018, contribution = 27.28%). For tensile and yield strength, extrusion temperature ( p = 0.009 for both, contribution = 9.60% &amp; 11.83%), layer height ( p = 0.004 for both, contribution = 11.86% &amp; 14.55%), speed (p = 0.004, contribution = 11.60% &amp; p = 0.007, contribution = 12.84%), and orientation ( p &lt; 0.001 for both, contribution = 63.49% &amp; 59.28%) are most significant. Five samples, chosen for superior elastic modulus and yield strength, undergo bending tests, exhibiting significant flexural strength without fracture. Findings indicate that precise control of FFF parameters enhances the mechanical properties of polymer‐based composites through AM technology. Highlights Surface‐textured composite via additive manufacturing Explores the effects of process parameters on the mechanical properties Precise control of FFF parameters enhances the mechanical properties. Highlights the importance of controlled fiber orientation

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)0.001
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 score0.106
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.016
GPT teacher head0.223
Teacher spread0.207 · 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

Citations9
Published2025
Admission routes1
Has abstractyes

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