Object Rotation and Translation for Serial Planar Acoustic Microassembly
Bibliographic record
Abstract
Damage-prone microscale products that need to be mass-assembled, such as surface mount devices, would benefit from being assembled in mid-air using acoustic forces. These forces can manipulate objects without contact, thereby circumventing damage. However, joining two objects together, which is a key step in assembly, is challenging to do acoustically because as the objects approach each other, the acoustic waves scattering off one object begins to greatly impact the position and orientation of the other object. Previous demonstrations of acoustic microassembly have demonstrated various techniques to overcome the acoustic scattering force, such as assembling, within a gel medium or using a robotic arm for object translation but there is still no way of achieving complex, configurable, 3-D assemblies in mid-air by purely using acoustic waves. To better maintain the object's orientation under a dominating acoustic scattering effect, we developed a new acoustic rotation algorithm called the two-segment phase gradient (TSPG) algorithm, which can rotate the object with an rms error of 12$^{\circ }$under open-loop control while maintaining the object's centroid with an root mean squared error (RMSE) of 0.41 mm. Next, we demonstrate an alternative technique to overcome the acoustic scattering force by using the widely used single trap algorithm to cause one object to jump toward another object at a specified position and orientation. Finally, we demonstrate how the TSPG algorithm and single trap algorithm can be used to generate complex and configurable 2-D assemblies.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".