Distributed recycling of waste polymer into RepRap feedstock
Bibliographic record
Abstract
Purpose A low‐cost, open source, self‐replicating rapid prototyper (RepRap) has been developed, which greatly expands the potential user base of rapid prototypers. The operating cost of the RepRap can be further reduced using waste polymers as feedstock. Centralized recycling of polymers is often uneconomic and energy intensive due to transportation embodied energy. The purpose of this paper is to provide a proof of concept for high‐value recycling of waste polymers at distributed creation sites. Design/methodology/approach Previous designs of waste plastic extruders (also known as RecycleBots) were evaluated using a weighted evaluation matrix. An updated design was completed and the description and analysis of the design is presented including component summary, testing procedures, a basic life cycle analysis and extrusion results. The filament was tested for consistency of density and diameter while quantifying electricity consumption. Findings Filament was successfully extruded at an average rate of 90 mm/min and used to print parts. The filament averaged 2.805 mm diameter with 87 per cent of samples between 2.540 mm and 3.081 mm. The average mass was 0.564 g/100 mm length. Energy use was 0.06 kWh/m. Practical implications The success of the RecycleBot further reduces RepRap operating costs, which enables distributed in‐home, value added, plastic recycling. This has implications for municipal waste management programs, as in‐home recycling could reduce cost and greenhouse gas emissions associated with waste collection and transportation, as well as the environmental impact of manufacturing custom plastic parts. Originality/value This paper reports on the first technical evaluation of a feedstock filament for the RepRap from waste plastic material made in a distributed recycling device.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".