Selection of optimal part orientation in fused deposition modelling using swarm intelligence
Bibliographic record
Abstract
This paper presents a swarm intelligence approach for optimal orientation of a part produced by the fused deposition modelling method, with a view to reduce the volumetric errors that are primarily responsible for poor surface finish. A part is oriented in such a way to assist the designer in reducing build time, while at the same time enhancing part quality and orientational efficiency. The conventional approach in optimization of part orientation considers only minimization of the staircase effect in order to enhance the part quality. In the approach used by the present authors, a part is oriented by transforming it about x and y axes and an orientation is selected that not only provides minimum volumetric error and building time but also enhances orientational efficiency of the fused deposition modelling method. Using the swarm intelligence technique, the hierarchical particle swarm optimization algorithm is employed to obtain optimal part orientation. The results obtained by using the hierarchical particle swarm optimization algorithm are further compared with the results obtained by using the genetic algorithm; the results reveal that the hierarchical particle swarm optimization algorithm provides a better outcome than the genetic algorithm. An example is given to illustrate the approach.
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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 | 0.001 | 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".