Investigating the impact of <scp>3D</scp> printing process parameters on the mechanical and morphological properties of fiber‐reinforced thermoplastic polyurethane composites
Bibliographic record
Abstract
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% & p = 0.019, contribution = 23.49%; layer height: p < 0.001, contribution = 34.33% & p = 0.008, contribution = 34.63%; orientation: p = 0.001, contribution = 46.11% & p = 0.018, contribution = 27.28%). For tensile and yield strength, extrusion temperature ( p = 0.009 for both, contribution = 9.60% & 11.83%), layer height ( p = 0.004 for both, contribution = 11.86% & 14.55%), speed (p = 0.004, contribution = 11.60% & p = 0.007, contribution = 12.84%), and orientation ( p < 0.001 for both, contribution = 63.49% & 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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".