Algorithm for Accumulating Part Mating Gaps to Evaluate Solid and Fluid Performances
Bibliographic record
Abstract
Abstract Part mating gaps need to be effectively adjusted during the assembly of products and greatly affect the working performance. This paper develops an algorithm to calculate the dimensional and aerodynamic indexes affected by part mating gaps. Mating gaps are expressed by displacements of one contact surface, and indexes are evaluated by displacements of key surface, when the surface is ready to compare with the other. A graph that denotes contacts as nodes and related paths through the physical domain as lines is proposed to express assembly sequences and hierarchies. A variable is defined to combine the time set with the displacement set. Boolean algebraic theorems are extended to derive a compact expression for the contact graph that supports the organization of accumulations at different surfaces with propagations through physical domains. Demonstrations of this method using three products exhibit the general applicability, and the application shows that the performance deviations of the centrifugal fan and axial turbine are apparent. In particular, the isentropic efficiency is good with a certain probability, despite turbines having mating gaps. The algorithm benefits both design and assembly: design can be performed through the fluid domain, which is affected by the mating gaps, and when the parts are being adjusted, the selected tolerance limit allows engineers to monitor key surfaces to ensure good aerodynamic performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".