Part consolidation for additive manufacturing: A multilayered topology optimization approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Part consolidation (PC) is becoming a viable cost savings approach due to the increased design freedom associated with industry adoption of additive manufacturing. However, there is little research focused on mathematical approaches for assembly level design generation, with most work aimed at providing best practices for merging several parts into one. This article presents a novel topology optimization approach to PC that determines the ideal number of parts, their geometry, and optimal joining pattern, without bias towards the original assembly. Multiple layered design domains are created, and a joining domain that determines the connections between parts is introduced. A multiobjective problem statement optimizes the complex trade‐off between compliance, support structure volume, surface area, and number of joints, to minimize the total cost of the final assembly. Design variable initialization and boundary condition placement are discussed for problems with multiple domains. Three test cases are presented and solved for a range of cost trade‐offs to demonstrate optimized solutions as design objectives are varied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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