Artefacts for optical surface measurement
Bibliographic record
Abstract
Flexible manufacturing technologies are supporting the routine production of components with freeform surfaces in a wide variety of materials and surface finishes. Such surfaces may be exploited for both aesthetic and performance criteria for a wide range of industries, for example automotive, aircraft, small consumer goods and medial components. In order to ensure conformance between manufactured part and digital design it is necessary to understand, validate and promote best practice of the available measurement technologies. Similar, but currently less quantifiable, measurement requirements also exist in heritage, museum and fine art recording where objects can be individually hand crafted to extremely fine levels of detail. Optical 3D measurement systems designed for close range applications are typified by one or more illumination sources projecting a spot, line or structured light pattern onto a surface or surfaces of interest. Reflections from the projected light are detected in one or more imaging devices and measurements made concerning the location, intensity and optionally colour of the image. Coordinates of locations on the surface may be computed either directly from an understanding of the illumination and imaging geometry or indirectly through analysis of the spatial frequencies of the projected pattern. Regardless of sensing configuration some independent means is necessary to ensure that measurement capability will meet the requirements of a given level of object recording and is consistent for variations in surface properties and structure. As technologies mature, guidelines for best practice are emerging, most prominent at the current time being the German VDI/VDE 2634 and ISO/DIS 10360-8 guidelines. This considers state of the art capabilities for independent validation of optical non-contact measurement systems suited to the close range measurement of table top sized manufactured or crafted objects.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.023 |
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".