Optimal part orientation in layered manufacturing using evolutionary stickers-based DNA algorithm
Bibliographic record
Abstract
Over recent years, layered manufacturing (LM) has been one of the most important emerging research areas, as well as practice perspective, owing to its capability to reduce the product development time, and therefore time-to-market. In LM, owing to the significant role played by the part orientation in the successful and efficient reduction of the staircase effect, the determination of optimal part orientation is a matter of paramount importance. In this research, the dual parameters problem has been modelled, taking into consideration the constraints pertaining to the rotation of the computer aided design (CAD) model about two axes, while aiming to optimize the objective function that involves layered process error as well as build time. The current paper presents an advanced stickers-based DNA algorithm (SDNA) inspired by the characteristics of deoxyribonucleic acid (DNA) as a tool to achieve the optimal orientation during fabrication of a part. The salient feature of the proposed algorithm is the use of stickers along with DNA memory strand, which are responsible for the representation of information. Moreover, fundamental operations are applied to manipulate the positions of the stickers in essentially all the possible ways. The performance of SDNA has been tested on two standard case studies and the comparisons are made with results obtained from genetic algorithm (GA). The results clearly demonstrate the efficacy of proposed algorithm over GA when applied to the underlying problems.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".