Visual measurement of MEMS microassembly forces using template matching
Bibliographic record
Abstract
This paper describes a new visual force sensing method for measuring microforces acting upon the jaws of passive microgrippers used in the assembly of MEMS devices. The importance of force measurement during microassembly is to confirm the microgripper-micropart make a successful grasp and to also protect the microparts and microgripper from excessive forces which may lead to damage during the assembly process. In the proposed approach, the force measurement problem is reduced to a problem of determining the microgripper jaw displacement. A 3-D finite element model is developed to study the relation between the forces and the displacements. The resulting nonlinear force-displacement relationship is fitted into a second degree equation. Computer vision is used to measure the relative displacements of the right and left microgripper jaws with respect to the microgripper base during assembly. Patterns that were introduced to the microgripper during the design phase are used to measure those relative displacements through pattern identification. Two-dimensional pattern identification is performed using normalized cross correlation template matching, to estimate the degree to which the image and pattern are correlated
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".