Multiscale Design Optimization with Integrating Overhang Constraints for Additively Manufactured Lattice Structures
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-1139.vid Topology optimization determines the optimal placement of material within a specified design space to maximize structural performance including its specific stiffness. Such design often results in geometrically complex structures that can be built employing additive manufacturing. To further increase the specific stiffness of structures, mesoscale periodic lattice structures can be implemented into intermediate density regions. Self-supporting structures are often desired to reduce sacrificial support material during additive manufacturing processes and are usually enforced by using an overhang constraint. However, as the overhang inclination angle is restricted during the optimization process, the design freedom and the specific stiffness of the components are often consequently lowered. This study explores the amalgamation of overhang constraints employing lattice structures. It is found that lattice structures could act as a support for more structurally dense material that lies above. This results in regaining some of the design freedom typically compromised when applying an overhang constraint in a typical topology optimization process. The benefit of lattice structure supporting overlying solid material is found to be optimal in a volume fraction range of 0.3-0.7; where volume fractions below or above this range often result in, respectively, lattice structure or solid material dominant designs. A case study is presented to demonstrate the proposed approach. In this case study, it is found that applying an overhang constraint to a traditional topology optimization problem increases the maximum deflection by 58.8% when compared to the topology optimization free of an overhang constraint. On the other hand, implementing lattice structures into the overhang constrained optimization problem results in only a 34.2% increase in the maximum deflection, displaying a recovery of 24.6% of the maximum deflection.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".