Disassembling Process Inference Using Positional Relations Matrix for Complicated Machines
Bibliographic record
Abstract
Disassembling of a part is required for maintenance of machinery in case. However, the disassembling process is often not explained in the operation manual, or the explanation of the disassembling does not cover all the situations of all the individual parts, even though, such disassembling could be dealt with by operators that are not familiar with the mechanism of machine. Operators themselves have to determine the disassembly process in such a case. Therefore, it is crucial to develop a system that helps inexperienced operators to find out a proper disassembling process. We focus on the disassembling of a specific part referred to as a target part. The approach is based on the positional relation information among the parts. The positional relations matrix that obtained from the contact states of any two parts in all possible directions and can be generated from the ordinary CAD data. This study proposed a method to infer a disassembly process of a specific part based on the positional relation matrix. The method deduces the disassembly process of the target part with the shortest steps, in the condition of one-part-at-a-time manner. We also introduced an integration of disassembling parts based on the obtained process. A case study was conducted and the result confirmed the feasibility of the proposed method; the effectiveness of the integration approach was also demonstrated.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".