Bibliographic record
Abstract
Purpose In furthering numerical optimization techniques for the light-weighting of components, it is paramount to produce algorithms that closely mimic the physical behavior of the specific manufacturing method under which they are created. The continual development in topology optimization (TO) has reduced the difference in the optimized geometry from what can be physically realized. As the reinterpretation stage inevitably deviates from the optimal geometry, each progression in the optimization code that renders the final solution more realistic is beneficial. Despite the efficacy of material extrusion (MEx) in producing complex geometries, select manufacturing constraints are still required. Thus, the purpose of this paper is to develop a TO code which demonstrates the incorporation of MEx specific manufacturing constraints into a numerical optimization algorithm. Design/methodology/approach A support index is derived for each element of the finite element mesh that is used to penalize elements, which are insufficiently supported, discouraging their existence. The support index captures the self-supporting angle and maximum allowable bridging distance for a given MEx component. The incorporation of the support index into a TO code is used to demonstrate the efficacy of the method on multiple academic examples. Findings The case studies presented demonstrate the methodology is successful in generating a resulting topology that is self-supporting given the manufacturing parameters specified in the code. Comparative to a general TO problem formulation, the optimal material distribution results in a minimally penalized design on a compliance normalization metric while fully adhering to the MEx specific parameters. The methodology, thus, proves useful in generating an infill geometry is fully enclosed regions, where support material extraction is not a possibility. Originality/value The work presented is the first paper to produce a novel methodology that incorporates the manufacturing-specific constraint of bridging distance for MEx into TO code. The results generated allow for the creation of printed components with hollow inclusions that do not require any additional support material beyond the intended structure. Given the advancement, the numerical optimization technique has progressed to a more realistic representation of the physical manufacturing method.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".