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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".