Studies on 3D Printability of Novel Impact Modified Nylon 6: Experimental Investigations and Performance Evaluation
Bibliographic record
Abstract
Abstract The development of functional polymer parts with complex geometrical shape has been possible through additive manufacturing. In this research work, the issues such as warping, filament clogging, and layer delamination related to fused deposition modeling‐based 3D printing of nylon 6 are focused and its poor printability is improved. Nylon 6 is toughened by blending with an impact modifier, ethylene terpolymer. Complex viscosity, storage, and loss moduli of modified nylon increase significantly, which help to improve 3D printability of nylon 6. 3D printing of modified nylon is performed at optimized conditions, such as printing temperature, print speed, bed temperature, and cooling speed. Izod impact strength of 3D printed nylon 6‐based blend is observed ≈543 J m−1, which is significantly higher than the value (48 J m−1) available in literature for injection molded pristine nylon 6. Thermomechanical analysis of modified nylon shows higher coefficient of linear thermal expansion in normal direction as compared to that in flow direction, which is due to the orientation of polymer crystals during processing. The voids are observed on the cross‐sectional surface of impact fractured sample, which are responsible to produce light‐weight 3D printed specimens.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".