Virtual Fixturing: Inspection of a Non-Rigid Detail Resting on 3-Points to Estimate Free State and Over-Constrained Shapes
Bibliographic record
Abstract
Abstract When the geometry of a non-rigid part or pre-assembly is measured fully clamped (over-constrained) in a measurement fixture, the spring-back information and influence from gravity forces are usually lost in the collected data. From the 3D-measurement data, it is hard to understand built in tensions, and the detail’s tendency to bend, twist and warp after release from the measurement fixture. These effects are however important to consider when analyzing each part’s contribution to geometrical deviations after assembly. In this paper a method is presented, describing how free state shape and over-constrained shape of a measured detail can be virtually estimated starting from acquired data when the part or the preassembly is resting on only 3-points. The objective is to minimize the information loss, to spare measurement resources and to allow for a wider use of the collected data, describing the geometry. Part stiffnesses, part to part contacts and gravity effects are considered in the proposed method. The method is based on 3D-scanning techniques to acquire the shape of the measured object. Necessary compensations for part stiffnesses and gravity effects are based upon Finite Element Analysis (FEA) and the Method of Influence Coefficients (MIC). The presented method is applied to an industrial case to demonstrate its potential. The results show that estimated over-constrained shapes show good resemblance with measurements acquired when part is over-constrained in its measurement fixture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".