Analysis and Design of Lattice Structures for Rapid-Investment Casting
Bibliographic record
Abstract
This paper aims to design lattice structures for rapid-investment casting (RIC), and the goal of the design methodology is to minimize casting defects that are related to the lattice topology. RIC can take full advantage of the unprecedented design freedom provided by AM. Since design for RIC has multiple objectives, we limit our study to lattice structures that already have good printability, i.e., self-supported and open-celled, and improve their castability. To find the relationship between topological features and casting performance, various lattice topologies underwent mold flow simulation, finite element analysis, casting experiments, and grain structure analysis. From the results, the features established to affect casting performance in descending order of importance are relative strut size, joint number, joint valence, and strut angle distribution. The features deemed to have the most significant effect on tensile and shear mechanical performance are strut angle distribution, joint number, and joint valence. The practical application of these findings is the ability to optimize the lattice topology with the end goal of manufacturing complex lattice structures using RIC. These lattice structures can be used to create lightweight components with optimized functionality for various applications such as aerospace and medical.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".