Modelling of Cusp Geometry in Additive Manufacturing Parts Using a Piece-Wise Polynomial
Bibliographic record
Abstract
The final dimensional and geometric inaccuracies, and the resulting high surface roughness of the products have been the major problems in employing Additive Manufacturing (AM) technologies. Most of commonly used Additive manufacturing (AM) technologies are developed based on a layer-based manufacturing process to fabricate 3D models. The main critical issue in AM which reduces the surface integrity of the final products is the stair case error which happens due to layer by layer manufacturing process. A new method is presented to model the surface roughness of FDM parts based on considering a new geometry for the cusps. Variety of observations were conducted to model the exact geometry of the cusp. Considering that cusp geometry affects the surface roughness directly, the new geometry was used to predict the surface roughness distribution as a function of layer thickness and surface angle of the final FDM products. The model was validated by designing a set of experiments using 3D measurements of the surface roughness under high resolution surface topography device and the predicted model was in a good agreement with the experimental results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".