Development of a mobile 3D printer and comparative evaluation against traditional gantry systems
Bibliographic record
Abstract
Fixed robots have dominated the market of additive manufacturing (AM), despite presenting several limitations, such as the stationary nature of these robots and the limited workspace. Mobile robots solve these problems as they can move freely in the printing area without being rooted to the ground. This allows mobile robots to print large-scale structures and print in places that are unsafe for humans to reach and deploy fixed robots. However, mobile robots suffer from poor positional accuracy. In this paper, we present an accurate mobile robot for material extrusion AM and discuss in detail the design of the mobile 3D printer and its components. This work is the first to rigorously compare the quality, accuracy, and mechanical properties of parts printed by the mobile 3D printer against those printed by gantry systems. Results show that the parts produced by the proposed system are comparable to those of a gantry system in certain aspects such as the overall quality and shape fidelity. Additionally, the accuracy exceeded the state-of-the-art of mobile 3D printing achieving low ranges of less than 0.5 mm. Moreover, the proposed system outperforms other plastic 3D printing mobile robots in literature, excelling in both quality and accuracy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".