Variation Analysis of Non-Rigid Assembly Using FEM and Fractals
Bibliographic record
Abstract
Many studies on the assembly of non-rigid parts suggest that the part variation affects the assembly dimensional quality. However, little is known about how the detailed microstructure of part variation influences the assembly dimensional quality. In this paper, a new method based on the finite element method (FEM) and fractal geometry is proposed to explore the influence of the part variation microstructure on the assembly dimensional variation. In the new method, a special fractal function, the Weierstrass-Mandelbrot (W-M) function, is used to extract and represent the characteristics of the part variation microstructure. FEM is applied to analyze the deformation of non-rigid parts by integrating the part variation microstructure. The contribution of the detailed part variation to the final assembly deformation is obtained by the influence coefficients method. The proposed method is implemented by using commercial software tools, ANSYS and Matlab. The proposed method is illustrated through a case study on an assembly of two flat sheet metal parts. This new approach should benefit high precision assemblies.
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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".