Assembly Level Topology Optimization Towards a Part Consolidation Algorithm for Additive Manufacturing
Why this work is in the frame
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Bibliographic record
Abstract
As the adoption of additive manufacturing continues to grow in the aerospace industry, part consolidation is an emerging design technique aimed at decreasing assembly cost. Significant research is focused on design for additive manufacturing principles and their integration into design generation tools such as topology optimization, while part consolidation research has been limited to heuristic guidelines. This work presents the extension of topology optimization to assembly design for the simultaneous optimization of structural performance and connection layout. This methodology uses multiple domains occupying the same space along with a single joining domain to represent the assembly design. The proposed approach allows for future extensions with the calculation of additive manufacturing part costs on an individual domain level. The methodology is tested on a numerical example demonstrating the variation in part geometry and number of parts as the emphasis on joining cost is varied.
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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 | 0.000 | 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 it