A Generalized Consolidated Topology Optimization and DfAM Design Approach and its Application for Assembly Design
Bibliographic record
Abstract
As additive manufacturing (AM) is widely adopted, there is a growing need for design for additive manufacturing (DfAM) tools and design methodologies.The increased design freedom allotted by AM has facilitated the adoption of topology optimization (TO) for AM.This presents an opportunity to introduce TO into DfAM best practices to improve assembly designs.A consolidated topology optimization and DfAM design approach for general assembly design is proposed.Unlike current DfAM methodologies, all critical aspects of assembly design are incorporated to ensure a fully optimized design.The efficacy of the consolidated design approach is demonstrated by its implementation for the redesign of a Bombardier business aircraft cockpit pedestal assembly.The manufacturing cost was reduced by 18%, satisfying the primary design objective.The installation cost will be greatly lowered due to a reduced assembly complexity: A major and minor part count and fastener count reduction of 17%, 89% and 56% was achieved.The current paper contributes to the applicability and efficacy of DfAM by outlining a generalized design procedure sufficiently complex and complete for industry level assembly design problems.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".