Mesh Optimisation for General 3D Printed Objects with Cusp-Height Triangulation Approach
Bibliographic record
Abstract
3D printing (3DP) is increasingly utilized to achieve quick turnaround on various geometric designs and prototypes, being the growing part of additive manufacturing technology (AMT). The 3DP technique effectively improves the production of complex models in terms of low-cost, time-consuming production, and with less material volume. The key to results optimisation with 3DP is the preparation of the geometry. The following techniques can effectively reduce the required time of the 3D printing process for complex and non-linear CAD files. The fused deposition modelling/fabrication (FDM/FFF) techniques become the first choice in many applications, including biomedical ones. Still, some obstacles exist in the geometry roughness and quality zones. This paper proposes an optimisation method for 3D printed shapes used in biomedical devices and instrumentation by minimising the support structure attached to the model using the FDM technique. In this research, we proposed a method for dynamic compensation against gravity-affected parts extended from the main object’s geometry using a forward planar learning (FPL) algorithm to minimise cusp height in 3D printed objects. After the slicing stage, the outcomes proved to be of good quality, optimised the object’s surfaces, and minimised the printing time by 32%–38%. The proposed method is promising in defining a better setting for slicing and toolpath for FDM 3D printing. However, this method was not tested on other 3DP methods (Stereolithography (SLA), Selective laser sintering (SLS), and Digital Light Processing (DLP)), as more verification efforts need to be done on these 3D printing processes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".