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Record W4414208460 · doi:10.3390/polym17182485

Effect of Printing Temperature on the Microstructure and Tensile Properties of Polylactic Acid–Magnetic Iron Composites Manufactured by Material Extrusion

2025· article· en· W4414208460 on OpenAlexaff
Meriem Bouchetara, Sofiane Belhabib, Alessia Melelli, Jonathan Perrin, Timm Weitkamp, Ahmed Koubaa, Mahfoud Tahlaiti, Mustapha Nouri, Sofiane Guessasma

Bibliographic record

VenuePolymers · 2025
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsCentre Technologique des Résidus IndustrielsUniversité du Québec en Abitibi-Témiscamingue
FundersAgence Nationale de la Recherche
KeywordsMicrostructurePolylactic acidExtrusionComposite numberUltimate tensile strengthPorosityOxideIron oxide

Abstract

fetched live from OpenAlex

In this study, we examined how printing temperature affects the microstructure and mechanical properties of polylactic acid (PLA) composite reinforced with iron oxide i.e., magnetite manufactured using a material extrusion technique. The composite was printed at temperatures from 185 °C to 215 °C. Microstructure analysis via synchrotron radiation X-ray microtomography revealed changes in both iron oxide and porosity contents within the printed structures. Mechanical testing results demonstrated a limited effect of the printing temperature on tensile performance. Finite element computation is considered to predict the elasticity behavior of the printed composite by converting 3D images into 3D structural meshes. When implementing a two-phase model, the predictions show a leading role of the iron oxide content, and an overestimation of the stiffness of the composite. A three-phase model demonstrates a better matching of the experimental results suggesting a limited load transfer across the PLA-iron oxide interface with Young's moduli in the interphase zone as small as 10% of PLA Young's modulus. Magnetic actuation demonstrates that experiments on PLA-iron oxide plates reveal a pronounced thickness-dependent limitation, with the maximum deflection observed in thin strips of 0.4 mm.

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 score0.011
Threshold uncertainty score0.483

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 head0.003
GPT teacher head0.181
Teacher spread0.177 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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