Polymer-Based Additive Manufacturing: Process Optimisation for Low-Cost Industrial Robotics Manufacture
Bibliographic record
Abstract
The robotics design process can be complex with potentially multiple design iterations. The use of 3D printing is ideal for rapid prototyping and has conventionally been utilised in concept development and for exploring different design parameters that are ultimately used to meet an intended application or routine. During the initial stage of a robot development, exploiting 3D printing can provide design freedom, customisation and sustainability and ultimately lead to direct cost benefits. Traditionally, robot specifications are selected on the basis of being able to deliver a specific task. However, a robot that can be specified by design parameters linked to a distinctive task can be developed quickly, inexpensively, and with little overall risk utilising a 3D printing process. Numerous factors are inevitably important for the design of industrial robots using polymer-based additive manufacturing. However, with an extensive range of new polymer-based additive manufacturing techniques and materials, these could provide significant benefits for future robotics design and development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".