Evaluation of the Algorithmic Error of New Specification Tools for an ISO 14405-1:2016 Size
Bibliographic record
Abstract
Due to machine tool imprecisions during manufacturing, the actual product cannot be the same as the nominal model. The product’s geometric variations influence the geometrical requirements of functionality and assembly [6, 8]; this remains a problem of industrial performance and plays a major role in the quality and cost of products; hence the need for a reliable strategy to evaluate errors in the final inspection of part quality. Among all the geometric characteristics, the circular characteristic is very common on most parts. Therefore, the measurement and evaluation of circularity with a high degree of accuracy is of utmost importance. Size, form and orientation are the basic descriptors of the geometric quality of the objects. The recent publication of ISO 14405-1: 2016 defines the size as the fundamental geometric descriptor; it described a new set of specification tools for the size of part characteristics that directly apply to the ideal geometry of the component [13]. These tools present new challenges for an inspector using a coordinate metrology system. The study of the influence of form defects on the identification of dimensional and geometrical requirements seems necessary. This paper studies four modifiers ISO 14405-1:2016 (Minimum circumscribed size (GN), Maximum recorded size (GX), least squares size Minimum (GG) and Minimum area (MZ)) will be studied. This paper presents simple and effective algorithms for evaluating the circularity error of a large number of points using four specification modifiers of ISO 14405-1:2016, and a study on the influence of measurement system strategies on different algorithms for the evaluation of these new specifications. An analysis software was developed to compare the sensitivity of different parameters (number of points, noise amplitude and circularity defect) on ISO 14405-1:2016 modifiers.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".