Application of fuzzy knowledge base for corrected measured point determination in coordinate metrology
Bibliographic record
Abstract
This paper describes an application of fuzzy logic for corrected measured point determination in coordinate metrology. The correction method works on a series of indicated points obtained by contact scanning of the measured surface with a spherical tip probe. The outline of the probe ball defines an arc for each measured point, each such arc being delimited by the points of intersection with the preceeding and the following arcs. As a first approximation the corrected measured point is estimated as the mid-point of the arc. The refinement to the method consists in determining an angular compensation to be applied to the mid-point estimation and calculating the associated indicated measured point coordinate values. To determine an angular compensation a rule-based approach to decision making using fuzzy logic techniques is proposed. In this approach, we consider imprecise vague knowledge as a set of rules linking a finite number of conditions with a finite number of conclusions. The representation of such imprecise knowledge by means of fuzzy linguistic terms makes it possible to carry out quantitative processing in the course of inference based on the compositional rule of inference that is used for handling uncertain (imprecise) knowledge, often called approximate reasoning or fuzzy reasoning. Such knowledge can be collected and delivered by a human expert (e.g., decision maker, designer, process planner, machine operator, etc.). For our case, this knowledge is expressed by a finite number of heuristic fuzzy rules of the Multiple Input Single Output type (MISO).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".