Gradient-Based Optimization of Component Layout: Addressing Accessibility and Mounting in Assembly System Design
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This article proposes novel methods for capturing and modeling critical physical assembly parameters, with a particular focus on mounting and accessibility requirements, for use in 3D assembly design optimization. The effectiveness of the proposed parameters is validated by integrating them into a gradient-based optimization framework and applying it to three increasingly complex test cases. The proposed framework simultaneously addresses essential assembly requirements: maximizing packing density, preventing part overlap, accommodating components on nonplanar mounting surfaces, maintaining proximity relationships, and ensuring accessibility for high-maintenance components. The results show that the method consistently generates densely packed, feasible layouts that satisfy all defined assembly requirements. This work establishes a strong foundation for practical assembly design optimization. Future efforts will focus on improving computational efficiency, scaling to larger and more diverse problems, and incorporating additional engineering considerations to further enhance the framework's industrial and academic utility.
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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 | 0.000 | 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