A Computer-Aided Design-Based Tolerance Analysis of Assemblies With Form Defects and Deformations of Nonrigid Parts
Bibliographic record
Abstract
Abstract Product assemblability and functional behavior are affected by geometric deviations. These deviations consist of manufacturing errors and part deformation defects caused by external loads. Taking the sources of deviations into account in tolerance analysis yields not only to more precise and reliable results but also to more complex tasks. In this context, the modeling of assembly parts with defects in a Digital MockUp (DMU) is quite promising. In this article, an integrated decision support tool is proposed to consider the causes of multiple defects, such as tolerances and external mechanical loads, in the tolerancing process. The worst-case concept and the small displacement torsor (SDT) are used to model rigid parts with orientation and positional defects. To model the part with form defects, random positions of each toleranced face points are determined using the Gaussian perturbation method (GPM) and considering the tolerance zone limits. A computer-aided design (CAD) method based on the B-spline interpolation allows the reconstruction of realistic surfaces of parts with form defects. Realistic configurations of nonrigid components subjected to external mechanical loads are determined using the finite element analysis (FEA). The realistic assembly configurations are performed by updating the mating constraints between planar and cylindrical parts. The proposed method considers all tolerance types on CAD models (positional, orientation, and form defects) and part deformations. The tolerance analysis is performed to check the compliance with the functional requirement (FR) and to correct the initial tolerance values. An industrial case study is used to validate the steps of the proposed tolerancing method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".