Design of a hybrid high-throughput fused deposition modeling system for circular economy applications
Bibliographic record
Abstract
In recent years, recycling of plastics has opened several doors of advancements in the field of additive manufacturing (AM). The process of fused deposition modelling (FDM) has already been utilized for reprocessing waste plastics into filaments and finally printing them into useful products. Yet another concept of extrusion additive manufacturing (EAM) is gaining lots of attention. In this work, a screw assisted system based on EAM is designed and installed alongside a pre-existing FDM system. This system is referred to as the direct FDM (DFDM) system throughout this work. The DFDM system used operates with a 1.75 mm nozzle and has the ability of giving a high throughput. The focus of this work is to use this hybrid system (combination of FDM and DFDM systems) to print both virgin as well as recycled plastics. The scope of this work is to use one technology (either FDM or DFDM) at a time and to use both simultaneously for multi-material printing in future. After several trials of printing and setting up some printing parameters, the proposed system has been able to print with virgin as well as recycled PLA.
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.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".