Models partition for 3D printing objects using skeleton
Bibliographic record
Abstract
Purpose It is a challenge to print a model with the size that is larger than the working volume of a three-dimensional (3D) printer. The purpose of this paper is to present a feasible approach to divide a large model into small printing parts to fit the volume of a printer and then assemble these parts into the final model. Design/methodology/approach The proposed approach is based on the skeletonization and the minima rule. The skeleton of a printing model is first extracted using the mesh contraction and the principal component analysis. The 3D model is then partitioned preliminarily into many smaller parts using the space sweep method and the minima rule. The preliminary partition is finally optimized using the greedy algorithm. Findings The skeleton of a 3D model can effectively represent a simplified version of the geometry of the 3D model. Using a model’s skeleton to partition the model is an efficient way. As it is generally desirable to have segmentations at concave creases and seams, the cutting position should be located in the concave region. The proposed approach can partition large models effectively to well retain the integrity of meaningful parts. Originality/value The proposed approach is new in the rapid prototyping field using the model skeletonization and the minima rule. Based on the authors’ knowledge, there is no method that concerns the integrity of meaningful parts for partitioning. The proposed method can achieve satisfactory results by the integrity of meaningful parts and assemblability for most 3D models.
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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 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".