Orientation Optimization in Layer-Based Additive Manufacturing Process
Bibliographic record
Abstract
The advent of Additive Manufacturing (AM) process has greatly broadened the machining methods. Compared to conventional manufacturing methods, the process planning for AM is totally different. It should avoid process-induced defects such as warpage of overhang features. Process planning for AM should also generate necessary support structure not only to support the overhang structure but also to minimize thermal warpage and residual stress. In order to do so, a general process planning for AM is put forward in this paper. Given a specific part, the first step is the determination of build orientation. The choice of build orientation is one of the critical factors in AM since the build time, the material consumption, the removal of support structure, the deformation within final parts, the mechanical performance, and the surface roughness are all related to the build orientation. This paper utilizes the genetic algorithm to optimize the build orientation by considering the minimum volume of the support structure and the minimum strain energy of a part under specific working conditions. First, a general and feasible process planning for AM is proposed. Then detailed process planning for the optimization of build orientation is developed. The volume of support structure and strain energy are considered independently and corresponding optimal build orientations are obtained through genetic algorithm. A single weighted aggregate optimization function is then constructed to optimize the volume of support structure and strain energy simultaneously. Finally, a bracket is used to verify the feasibility of the proposed method.
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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 | 0.000 |
| 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".