Evaluating subassembly stability using stability directed subgraphs
Bibliographic record
Abstract
This paper presents a graph–theoretic approach to compute the degree of stability of the subassemblies formed during assembly sequence generation in Computer–Aided Assembly Planning (CAAP). A first stage of the approach builds a connected stability directed graph, which is a subgraph of the complete graph representation of the product. Vertices of this directed subgraph are the produc's parts and directed edges show explicitly which parts are stabilised by which other. The concept of stability matrices, also described in this paper, is used by an algorithm that builds the stability directed subgraph. Once the stability directed subgraph has been constructed, the new information that it contains can be processed in order to estimate subassembly's stability. In particular, by mapping every possible disassembly operation to every possible cut in the stability directed subgraph, the stability of the newly generated subassemblies can be estimated from an analysis of the direction and number of broken directed arcs in the cut. Examples of application of the model to investigate potential stability problems at assembly time is also presented.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".