3D-printed torsional mechanism demonstrating fundamentals of free vibrations
Bibliographic record
Abstract
Commercially available turn-key systems are expensive and require substantial lab space, making it harder to accommodate many in vibrations laboratories. This study presents a low-cost, compact, and portable torsional mechanism incorporating multiple rotating disks and a long thin rod supported vertically with bearings and fixed supports at the top and bottom ends to study the modeling of systems using experimental data. The mechanism consisting of a rod, disks, bearing, and disk supports, and the base is built by 3D printing using thermoplastic PETG. The long, thin rod in this mechanism serves as a torsional spring. The equivalent stiffnesses of the 2 DOF system can be changed by adjusting the vertical position of the disks with respect to the ends, thereby shortening or lengthening the effective twist length of the thin rod. The overall dimensions of the mechanism are 6 inches in height, 5 inches in width, and 2 inches in depth, and the expected cost including the experimental setup is around USD$30 if an Arduino is used for data acquisition and $170 if equipped with a National Instruments external data acquisition card. Learning objectives of the lab course utilizing the proposed mechanism are identified. The free response data are collected for a single degree-of-freedom system using an external data acquisition card and potentiometer and unknown parameters of the system are determined by system identification. Mechanism unknown parameters are calculated using system identification and a theoretical model is compared with the experimental data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".