Automatic Microassembly Using Visual Servo Control
Bibliographic record
Abstract
We propose an automatic microassembly method that can be used to construct three-dimensional microelectromechanical system (MEMS) structures. A six degree-of-freedom micromanipulator, equipped with a passive microgripper, is employed to grasp, manipulate, and join the micropart using visual feedback from an optical microscope. The proposed process utilizes a two-stage alignment strategy to perform the micro-grasping and micro-joining tasks. Using a vision-based localization method, the Cartesian coordinates of the manipulated micropart in three-dimensional space are determined. Further, a vision-based contact sensor determines the contact state between two micro-components in three dimensions to facilitating the micro-joining tasks. Visual servo control is used for accurate position feedback in three Cartesian coordinates during microassembly tasks. The necessary steps towards construction of complex three-dimensional MEMS devices, i.e., grasping a micropart, manipulating it, joining it to another micropart, and finally releasing it from the microgripper, have been successfully carried out using a six degree-of-freedom micromanipulator. Experiments demonstrate both the efficiency and validity of the proposed automatic assembly approach.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".