Constrained Topology Optimization For Additive Manufacturing Of Structural Components In Ansys®
Bibliographic record
Abstract
Topology Optimization is currently the main technique to optimize an objects structural design.This method commonly produces parts that have exceedingly complex geometry.Additive manufacturing (AM) is the main manufacturing process to produce these optimized designs due to the flexibility and speed it offers.However, results of topology optimization without considering manufacturing process limits, even AM ones, may result in designs that are expensive and difficult to build.This paper presents a topology optimization filter that minimizes the effect of overhang structures.These structures are very difficult to manufacture using conventional AM techniques.In order to constrain the gradient compliances with respect to densities and converge the results towards a structure with the least amount of overhang structures, sensitivities are modified using the proposed filter.To implement the proposed filter and the base topology optimization methods ESO and SIMP, ANSYS Parametric Design Language (APDL) is employed within the ANSYS ® Workbench™ environment.The results of a case study using the different topology optimization methods are investigated.Finally, an implementation of the proposed AM filter is used to solve an MBB-beam problem.The result is a structure that needs the least amount of support structure.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".