Identification of correlated characteristics in a linear statistical tolerance design
Bibliographic record
Abstract
Abstract:- In order to study the variations of mechanical components of an assembly, the accumulation of tolerances may be calculated using two major approaches: the Worst Case method and the Statistical (or probabilistic) method. The Worst Case method is very simple and well known. It must be applied only for simple assemblies where a larger allowance of the available space is granted for the tolerances. The statistical approach allows us to assign bigger tolerances for each component by taking advantage of random phenomena, which may occur during the manufacturing and assembly. On the other hand, this approach implies several hypotheses which may not always be respected in reality. This article proposes a case study to model a mechanical assembly of electrochemical cells whereby each cell consists of multiple layers of various materials. The first part of the study describes our main working hypothesis that encompasses the variability of environmental conditions (such as temperature, charge, pressure, shape defects, etc.) that necessitated the introduction of corrective semi-empirical factors. The second part contains the mathematical model, which describes the stochastic behavior of the thickness of cells once they are assembled. This model integrates the variance of each of the materials and the resulting effects of correlation between the materials, as well as the effects of the auto correlation into the case of several layers of the same material. The study demonstrates that the correlation and the auto correlation combine with different capability indices allows more precise predictions during the modeling stage. This allows the designer to optimize the parameters of the design to maximize the mechanical and energy performances of the electric cells.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".