Parallel microassembly with a robotic manipulation system
Bibliographic record
Abstract
This paper proposes a methodology to assemble multiple micro-components simultaneously with a robotic manipulator using a parallel assembly method. Through manipulating and assembling the micro-components, intricate, out-of-plane, three-dimensional micro-devices can now be fabricated. Use of a parallel microassembly process rather that a serial approach can significantly increase the productivity and reduce the cost of assembling micro-devices. The parallel microassembly operation proposed in this work was developed and implemented on a 6-DOF robot manipulator to attain considerable manufacturing flexibility. In this study, three passive microgrippers were bonded in parallel to the end-effector of the manipulator. Three microparts were then grasped by the grippers from the worktable of the manipulator, rotated 90°, and assembled onto the base substrate simultaneously. During the parallel microassembly operation, the visual image may not be able to monitor all three gripper-part pairs simultaneously due to the limited field of view of the microscope. Through the use of an alignment-calibration algorithm with only one gripper-part set, the remaining two sets were successfully manipulated and inserted onto the desired assembly location.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".