A study on revolute joints in 3D-printed non-assembly mechanisms
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore a new design for the journal of revolute joints that can improve the dynamic performance of 3D printed non-assembly mechanisms. Design/methodology/approach The design improves upon previous proposed designs that use drum-shaped journals in place of cylindrical ones. The authors introduce an innovative new worm-shaped journal. The authors also propose a systematic and efficient procedure to identify the best parameter values for defining the exact shape of the journal. Using three different mechanisms for the experiments, the paper constructs 3D computer-aided design (CAD) models using the design as well as cylindrical and drum-shaped designs. The parameters for the optimum geometry for each type of design are determined by dynamic simulation using the CAD system. Actual prototypes of the ideal designs are constructed using a commercial fused deposition modeling (FDM) machine for physical comparisons. Findings This paper shows that in simulations as well in physical models, the proposed design outperforms the previous designs significantly. Research limitations/implications This study was mainly focused on the FDM process, and the authors have not yet explored other processes. One limitation of this approach is that it requires the mechanism to be printed along the axial direction of the revolute joint. Originality/value This paper proposes a new design for the journal in 3D printed revolute joints. A clear advantage of the design is that it can easily be used to replace normal revolute joins in non-assembly models without affecting any other parts of the geometry. Therefore, with relatively little effort, the authors can print non-assembly mechanisms with improved dynamic performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".