Utilization of Low Pass Filters for the Calculation of Termination Points for 3D Fabrication Control of Pipe Spools
Bibliographic record
Abstract
In the context of prefabrication and modularization, termination points are defined as local coordinate systems where assemblies are either connected or constrained. These points are typically points of connection between assemblies, sub-assemblies, or modules. As such, it is critical to ensure that termination points are measured accurately. In this study, the impact of point cloud filtering as a pre-processing step for improving the accuracy of detecting termination points in point clouds is investigated. An industrial-scale experiment was conducted where 3D scans of 40 piping components were collected and analysed while being fabricated. For data collection, each piping object was scanned using a laser scanner as well as a SLAM (simultaneous localization and mapping) scanner (80 point clouds were collected in total). The components vary in their design geometry. Using a guided Hough transform, a circle fitting method was developed to find the termination points in the scanned point clouds. It was then shown that applying noise removal as a pre-processing step for the termination point calculation can substantially improve the accuracy irrespective of the source of acquisition.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".