Vision-based measurement of microassembly forces
Why this work is in the frame
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Bibliographic record
Abstract
This work describes a vision-based force sensing method for measuring microforces acting upon the jaws of passive, compliant microgrippers, used to construct 3D microstructures. The importance of jaw force measurement during microassembly is to confirm that the microgripper–micropart makes a successful grasp and to protect the microparts and microgripper from excessive forces which may lead to damage during the assembly process. Finite-element analysis of the microgripper is performed to determine the relation between the displacement and the resultant forces of its jaw. The resulting nearly linear force–displacement relationship is fitted to a first-degree equation. A mathematical model of the microgripper system validated this force–displacement relation. The proposed vision-based gripper force measurement techniques determine the deflections of the microgripper jaws during the microassembly process. The deflections in the gripper jaws are measured during the microassembly processes through computation of the relative displacements of the right and left microgripper jaws with respect to the microgripper base. Two approaches are proposed. The first approach uses pattern identification to measure these relative displacements. Two-dimensional pattern identification is performed using normalized cross-correlation to estimate the degree to which the image and pattern are correlated. The second approach uses object recognition and image processing methods, such as zero-crossing Laplacian of Gaussian edge detection and region filling. Experiments performed confirm the success of both approaches in measuring the microgripper jaw deflections and therefore the assembly forces.
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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 it