Bibliographic record
Abstract
Laser scanning, a widely used technology, has been highly developed and adopted in various industrial applications. The methodologies used for scanner date processing are mostly point based. In this thesis, a new approach is presented to analyze spatial data obtained from a 3-D laser scanner for shape error inspection. Different from traditional methodologies, the method proposed in this research is frequency based. The method utilizes the Fourier transform to decompose a 2-D curve or 3-D shape into its spatial components by applying two 1-D FFT (Fast Fourier Transform) on 2-D curves or two 2-D FFT on 3-D shapes. The spatial components including frequency, amplitude, and phase are defined as shape characteristics to represent the shape under inspection. By relating spatial components with GD&T (Geometric Dimensioning and Tolerancing) standards using proper analysis techniques, such as frequency spectrum and cross correlation, shape errors can be detected and characterized. One of the applications of this method is automated inspection. In this research, the spatial data method is applied to MIG (Metal Inert Gas) weld inspection. Experiments are carried out to analyze the 2-D curve of a projection weld data, and the 3-D scanning data directly. A MIG weld inspection system is also developed for production use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".