Examination and Improvement of Direction-Dependent Surface Properties in Composite Structures Produced by the Fused Depostion Modelling Method
Bibliographic record
Abstract
Additive manufacturing methods, which have begun to be used in final product production beyond producing prototypes, are popular research topics today. The Fused Filament Fabrication (FFF) method, which has a wider usage area and user base, is the most well-known among these methods. The method in which the polymer is extruded in layers has advantages such as design freedom and topology optimization, as well as disadvantages such as surface roughness and low production speed. The number of materials that can be used in the FFF method is diversifying day by day, and polymeric composites can also be produced beyond pure polymers. The use of engineering polymers such as polyamide in this production method creates many new opportunities. In addition, the start of production of short fiber reinforced polymeric composites has paved the way to produce high-performance final products. In this study, the effects of parameters such as layer thickness and printing orientation on the surface roughness of samples produced using polyamide and short carbon fiber reinforced polymer matrix composite materials were examined. Chemical surface treatment was applied to the surfaces of 3D-printed samples to improve surface roughness. It was concluded that the increase in layer thickness increases the surface roughness, the -/+45 filling orientation creates higher roughness than the 0 and 90 orientations, and the surface quality can be increased by chemical surface modification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".