Using 3D Scanning for Accurate Estimation of Termination Points for Dimensional Quality Assurance in Pipe Spool Fabrication
Bibliographic record
Abstract
Increased prefabrication and modularization have resulted in fabrication shops producing more complex assemblies with tighter tolerances. Most measurements in fabrication shops are still done using manual tools that are not accurate enough for engineering tolerance specifications, which can lead to rework. Three dimensional (3D) scanning and measurement systems can provide increased accuracy and digital integration capabilities, however they do not sufficiently support fast and accurate dimensional quality assurance (DQA) of pipe spool fabrication. This is because no dimensional quality assurance methods to date have focused solely on termination points for pipe spool assemblies. In the present article, a new scan-vs-BIM method is developed to accurately estimate termination points for 3D scanned cylindrical assemblies. This method relies on statistically fitting circular features at termination points and thus eliminating conventional issues with target placement for laser trackers and measurement readings for tape measures. The method is tested in an industrial-scale experiment, where 30 pipe spool assemblies were fabricated, and more than 400 quality control steps completed. The accuracy of termination point detection was benchmarked against results from a laser tracker and compared against commercial scan-to-BIM software. Results show that the developed method has an average accuracy of 1.01 mm and is significantly better than the scan-to-BIM software with an average accuracy of 4.75 mm.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".