A novel setup for in-process geometric inspection of 3D printed parts viastructured-light 3D scanning
Bibliographic record
Abstract
In-process 3D scanning of 3D-printed parts offers the potential to improve the accuracy and efficiency of additive manufacturing. In this work, a setup for in-process geometric inspection of 3D printed parts of fused filament fabrication (FFF) that combines a 3D printer and a 3D scanner is presented, as well as a software package developed in Python. The software transforms the 3D scanned point cloud to the 3D printer's reference system and produces a deviation field by comparing the outer surface of the 3D printed part to the reference geometry. By performing the scanning while the part is being printed, it is possible to monitor the process in real-time and detect any errors that may occur. The main application for this system is to understand the geometric deviation of the 3D printed parts, which allows us to reduce systematic deviations without the need for sacrificial parts or machine calibration. This system allows for the generation of an accompanying 3D model that can be used for geometric inspection or simulations at the end of the printing process. In conclusion, in-process 3D scanning holds promise as a valuable tool for improving the accuracy and efficiency of 3D printing, and there are many opportunities for further optimization and development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".