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Record W4407955578 · doi:10.1016/j.procir.2024.09.004

Optimizing the properties of PHBV/PBAT blend for additive manufacturing

2025· article· en· W4407955578 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProcedia CIRP · 2025
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Guelph
FundersGenesis HealthCare SystemNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceBusinessProcess engineeringEngineering

Abstract

fetched live from OpenAlex

In recent years, 3D printing has become increasingly popular for producing biodegradable products and for exploring a variety of applications. The development of polyhydroxy-co-3-butyrate-co-3-valerate (PHBV) and Polybutylene adipate-co-terephthalate (PBAT) blend for 3D printing has been the subject of extensive research. This paper examines the impact of printing parameters on the mechanical properties of 3D-printed components. The study successfully optimized the mechanical properties of 3D-printed PHBV/PBAT blend parts, achieving a tensile strength of 22.96 MPa, a modulus of 767 MPa, and an elongation percentage of 180 %. These results were obtained by identifying the optimal printing parameters through a Taguchi L9 design and Desirability Function Analysis (DFA), specifically a 0.35 mm layer height, 210 degrees C nozzle temperature, and 100 % infill density. The addition of PBAT significantly enhanced the ductility of PHBV, marking a substantial improvement in elongation. This research provides a robust framework for optimizing the performance of biodegradable blends in additive manufacturing, with promising implications for future applications in sustainable material development.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score0.479

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 head0.015
GPT teacher head0.213
Teacher spread0.198 · 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