Topology and Build Orientation Optimization for Additive Manufacturing: Influence of Printing on Raft and Build Plate
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
<div class="section abstract"><div class="htmlview paragraph">As additive manufacturing technology advances, it is becoming a more feasible option for fabricating highly complex, lightweight structures in the automotive industry. To take advantage of the improved design freedom and to reduce support structures for the selected printing orientation, components must be designed specifically for additive manufacturing. A new approach for accomplishing this process combines topology and build orientation optimization, which aims to simultaneously determine the ideal build direction and component design to maximize stiffness and reduce additive manufacturing costs. Current techniques in literature are formulated for specific categories of additive manufacturing: either methods that print on a support structure raft or print directly on the build plate. However, these two categories have very different relationships between part orientation and support structure, resulting in distinct optimal orientations for each additive manufacturing category. This work proposes a flexible overhang area calculation methodology that can be applied to either additive manufacturing category, by deriving an element-level indictor that determines whether a given element is located on the build plate. The approach is integrated into a combined topology and build orientation framework that minimizes compliance and overhang area with a volume fraction constraint. An automotive control arm test case is used to validate the effectiveness of the proposed approach, comparing a baseline optimized design to overhang-minimized designs. The optimized orientations and topologies varied significantly when designing for additive manufacturing methods that print on a raft compared to the build plate, demonstrating the importance of considering this distinction.</div></div>
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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