Bibliographic record
Abstract
By selecting the optimal build angle, the surface roughness of rapid prototyped parts can be minimized. The objective of this study is to develop a model for estimation of surface roughness as a function of build angle and other build parameters for parts built by Fusion Deposition Modeling (FDM) machines. For that purpose, principles of the FDM technique, along with other rapid prototyping techniques, are reviewed and various standards for surface roughness measurements are introduced. Different analytical models for the estimation of surface roughness for FDM systems, which were proposed in the literature, are reviewed and reformulated in a standard format for comparison reasons. A new hybrid model is proposed for analytical estimation of the surface roughness based on experimental results and comparison of the models. In addition, Least Square Support Vector Machine (LS-SVM) is applied for an empirical estimation of the surface roughness. Robustness of the LS-SVM model is studied and its performance is compared to the hybrid model. The experimental results confirm better results for the LS-SVM model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".