Fast Assembly Tool Reasoning Based on Geometric Accessibility Analysis
Bibliographic record
Abstract
A product assembly or disassembly is completed by means of proper tools. The selection of feasible tools is an important process in planning a complete assembly or disassembly sequence. A key tooling consideration in assembly or disassembly planning is to reason the available space for a tool application during the assembly or disassembly of a product. Currently, assembly tool reasoning about space mainly depends on simulation-based or user-interactive approaches because of its computational complexity. These approaches are not proper in dealing with various what-if scenarios regarding assembly or disassembly planning in a rapid product development. They also depend on users’ expertise or experience in assembly or disassembly. This paper presents an efficient method of the geometric accessibility analysis for fast assembly tool reasoning. Techniques described in the paper are advantageous not only in the aid of generating a complete assembly or disassembly plan but also in the efficient support of such systems as computer-aided assembly planning (CAAP), design for manufacturing (DFM), design for assembly (DFA), design for disassembly (DFD), and computer-aided tool selection (CATS).Copyright © 2005 by ASME
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".