A Post-Topology Optimization Process for Overhang Elimination in Additive Manufacturing: Design Workflow and Experimental Investigation
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it