Intelligent 3-D sensing in automated manufacturing processes
Bibliographic record
Abstract
This paper focuses on the design of an intelligent, three-dimensional (3-D) sensing system applying artificial intelligence methodologies for quality assurance in automated manufacturing processes. An efficient 3-D object-oriented knowledge base and reasoning algorithm is developed. The knowledge base includes knowledge concerning the products, manufacturing processes, and inspection methods. The products knowledge base contains properties design and manufacturing. The manufacturing and inspection knowledge bases include various manufacturing techniques, criteria for detection and diagnosis of defects, and standards and limitations on various decision-making actions. A fast and reliable assurance of product quality may be achieved through fault detection and diagnosis, using symbolic knowledge processing combined with numerical analysis of data. Incorporated with the reasoning algorithms, the knowledge base assists in the design process anticipating manufacturing problems and assuring specified end product properties. The knowledge base is regularly updated using feedback of the inspection results. An inexpensive and accurate, non-contact 3-D range data measurement system is developed. In this system, multiple laser light stripes are projected onto the product and a single CCD camera is utilized to record the scene. The distortions in the projected line pattern are due to the orientation variations and surface curvature of the object. Utilizing a linear relation between the projected line distortion and surface depth, range data is recovered from a single camera image. The surface terrain information may be converted into the curvature, orientation, and depth of the shape to incorporate into the symbolic 3-D object-oriented knowledge base and reasoning algorithms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".