Multi-Material and Multi-Joint Topology Optimization for Lightweight and Cost-Effective Design
Bibliographic record
Abstract
Abstract Lightweighting and cost reduction are overarching research themes in aerospace and automotive industries, leading to the exploration of new materials, advanced manufacturing methods, and design optimization algorithms. Multi-material topology optimization is an important tool that can generate unconventional designs leveraging the differing mechanical properties of multiple material types to increase performance. However, these approaches do not consider joining design during optimization, which can ultimately result in higher cost, worse performance, and unrealistic designs that must be altered in the interpretation stage. This work presents a multi-material and multi-joint topology optimization methodology that models joints at the interfaces between dissimilar materials, controls the joining pattern using joint design variables, and reduces cost through the addition of a joining cost constraint. Design variable interpolation schemes, interface detection for unstructured meshes, and sensitivity analysis are outlined in detail in this paper. The approach is applied to a real-world rocker arm geometry to demonstrate the importance of considering joints during multi-material topology optimization. The results of the numerical example indicate that the methodology can successfully detect interfaces in unstructured meshes and strategically place joints to maximize stiffness of the structure. A parameter study of various joining cost constraint levels illustrates how the optimizer alters part topology and joining design to reduce cost.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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 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".