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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".