A Post-Topology Optimization Process for Overhang Elimination in Additive Manufacturing: Design Workflow and Experimental Investigation
Bibliographic record
Abstract
Abstract Although structural design complexities do not potentially pose challenges to many additive manufacturing technologies, several manufacturing constraints should be considered in the design process. One critical constraint is the structure's unsupported or overhanging features. If these features are not reduced or eliminated, they can cause a decline in part surface quality, inhibit print success, or increase production time and cost due to support removal. To eliminate these features, a new post-topology optimization strategy is proposed. The design problem is first topologically optimized, then boundary identification and overhang detection are carried out. Next, additional support-free struts subject to a specified thickness and angle are introduced to support previously detected infeasible features. This addition can increase the structure’s volume; therefore, an optional volume correction stage is introduced to obtain a new but lower volume fraction which will be used in the final topology optimization, boundary identification, and overhang elimination stages. Experimental and numerical load-displacement relationships are established for varying overhang angle thresholds and minimum feature sizes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".