A dynamic dimensional accumulation with propagations of part mating gaps for series-parallel assembly operations
Bibliographic record
Abstract
The changing spring-back effects of part mating gaps restrict the control of the overall dimensional error for the parts assembled by many sequential and parallel processes. So far, it has been merely adjusted by component positions or compensated by some joining processes, which is still limited and lack of a theory and tool for the general dynamic process. To obtain the dimensional response of current assembly when the part contacts with the other, this paper proposes algebraic modelling for dimensional error accumulations that organize the propagation through every part. A new contact graph firstly expresses the part liaisons. Then, a variable array encodes the displacements and the time when each contact finishes, which yields algebraic theorems for the conversion of contact graph to algebraic expression, and for estimating dimensional distortions. If propagations take finite element methods, the algebraic expression outputs the coupled spring-back effects for the current assembled parts that support both variation and tolerance analyses. The dynamic accumulation with propagations is validated by case studies, and the comparison indicates that the assembly with the largest number of parallel modes has the highest nonlinear variations caused by locating errors.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".