Topology Optimization of Cracked Structures With Manufacturing Constraints
Bibliographic record
Abstract
Abstract Topology optimization (TO) is a tool used in the design process to improve the structure’s performance, e.g. increasing the stiffness. However, the final topologies typically include complex shapes making them impractical or unrealizable from a fabrication point-of-view. This issue stems from the limitations that the currently available manufacturing methods have, such as milling. Hence, considering these limits as the manufacturing constraints into the optimization problem would shorten the post-processing time. Moreover, the existence of initial damage such as cracks is another common issue of engineering structures, resulting in unreliable topologies if neglected. These two challenges are the main motivations of this study. A projection method is utilized to restrict the range of solutions to a manufacturable design domain by projecting the design variable into a pseudo-density field. The main advantage of this approach is that the manufacturing restrictions are achieved naturally, without adding constraints to the TO problem, which leads to a straightforward calculation of sensitivities. This technique allows the designer to define, for instance, a minimum member size as well as a minimum hole size as geometrical constraints. In order to penalize the intermediate phases, the popular Solid Isotropic Material with Penalty method (SIMP) is employed. The Peri-dynamics theory is used to study the structure’s behaviour because of its ability to introduce discontinuities without requiring complicated mathematical expressions as opposed to its counterpart mesh-based approaches. The robustness of the proposed method is demonstrated through some compliance minimization problems.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".