Printing with mechanically interlocked extrudates using a custom bi-extruder for fused deposition modelling
Bibliographic record
Abstract
Purpose This paper aims to target to print functionally gradient materials (FGM) devices made of immiscible polymers in multi-material fused deposition modelling (FDM) systems. The design is intended to improve adhesion of dissimilar thermoplastics without the need for chemical compatibilization so that filaments from many different sources can be used effectively. Therefore, there is a need to invent an alternative solution for printing multiple immiscible polymers in an FDM system with the desired adhesion. Design/methodology/approach In this study, the authors have developed a bi-extruder for FDM systems which can print two thermoplastics through a single nozzle with a static intermixer to enhance bonding between input materials. The system can also change the composition of extrudates continuously. Findings The uniqueness of this extruder is in its easy access to the internal channel so that a static intermixer can be inserted, enabling deposition of mechanically interlocked extrudates composed of two immiscible polymers. Without this intermixer, the bi-extruder extrudes with simple side-by-side co-extrusion having no mechanical interlocking. The bi-extruder was characterized by printing objects using pairs of materials including polylactic acid, acrylonitrile butadiene styrene and high impact polystyrene. Microscope images of the cross-sections of the extrudates confirm the ability of this bi-extruder to control the composition as desired. It was also found that the mechanically interlocked extrudates composed of two immiscible polymers substantially reduces adhesion failures within and between filaments. Originality/value In this study, the first-ever FDM extruder with a mechanical blending feature next to the nozzle has been designed and used to successfully print FGM objects with improved mechanical properties.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".