Using Genetic Algorithms to Optimize the Build Orientation for Fused Deposition Modeled Components Containing Internal Reinforcement Structures
Bibliographic record
Abstract
Fused Deposition Modeling (FDM) is an additive fabrication process that builds a part from extruded filaments of a melted thermoplastic. Typically, the parts are built using a ‘solid’ (complete fill) or ‘shell’ (3–4 mm external boundary with a loose internal weave) strategy. The introduction of parametric internal structures to support the required tensile or compressive loads provides an intermediate solution to the standard build options, and reduces the material usage while reinforcing the part as required. The internal structures can have a hexagonal, pyramidal, or orthogonal configuration. Because of the configuration variation, the internal structure form arrangement and geometric structure will influence the optimal build orientation. This will have an effect on the productivity or build time, mechanical properties such as strength, surface finish, materials usage and the total build cost. This paper presents a model to optimize the orientation of a part for FDM fabrication while considering these various factors. The CAD part model (in STL format) is an input to the system. A genetic algorithm is used to obtain optimum orientation of the parts for FDM. The objective function for optimization is considered a weighted average of the performance measures such as build time, part quality, material usage, surface finish, interior geometry, strength characteristics, and related parameters. The merits of the approach will be demonstrated using models with varying levels of complexity. The final model tested consists of a human tibia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".