Improving Geometric Accuracy of 3D Printed Parts Using 3D Metrology Feedback and Mesh Morphing
Bibliographic record
Abstract
Additive manufacturing (AM), also known as 3D printing, has gained significant interest due to the freedom it offers in creating complex-shaped and highly customized parts with little lead time. However, a current challenge of AM is the lack of geometric accuracy of fabricated parts. To improve the geometric accuracy of 3D printed parts, this paper presents a three-dimensional geometric compensation method that allows for eliminating systematic deviations by morphing the original surface mesh model of the part by the inverse of the systematic deviations. These systematic deviations are measured by 3D scanning multiple sacrificial printed parts and computing an average deviation vector field throughout the model. We demonstrate the necessity to filter out the random deviations from the measurement data used for compensation. Case studies demonstrate that printing the compensated mesh model based on the average deviation of five sacrificial parts produces a part with deviations about three times smaller than measured on the uncompensated parts. The deviation values of this compensated part based on the average deviation vector field are less than half of the deviation values of the compensated part based on only one sacrificial part.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".