Effect of Adaptive Slicing on Surface Integrity in Additive Manufacturing
Bibliographic record
Abstract
In today’s Additive Manufacturing (AM), a part is typically manufactured using layer by layer addition of material from a Computer Aided Design (CAD) model. Traditionally the CAD model is transferred to RP system after exchanging to Stereo Lithography (STL) format which is triangulated tessellation of the CAD model. Then it is sliced using different slice algorithms and machine constraints. The inherent uncertainties in this process have led to development of adaptive direct slicing technique. There are several adaptive slicing techniques but only few researches have been done to calculate an actual surface error factor and the cost aspect of the slicing algorithm. This paper proposes new adaptive algorithm to compute a surface error factor and to find the cost effective approach for slicing. The adaptive slicing algorithm dynamically calculates slice thickness and it is based on the allowable threshold for surface integrity error to optimize the cost and time. The paper also provides comparative study of previously developed adaptive models by the authors based on cusp height and surface integrity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".