Using Genetic Algorithms to Optimize the Build Orientation for Fused Deposition Modeled Components Containing Internal Reinforcement Structures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it