Additively manufactured conformal cooling channels through topology optimization
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Cooling channels play a critical role in various casting and molding processes, impacting both the cycle time and quality of the product. As additive manufacturing technologies become increasingly prevalent, conventional straight-drilled channels are being progressively substituted by intricate cooling lines that conform to the contours of the fabricated part. This transition can lead to a significant reduction of the solidification time and temperature gradients, consequently lowering the occurrence of part defects. However, designing such channels becomes challenging as geometric complexity and manufacturing constraints increase. In this work, we present a density-based topology optimization approach to generate conformal cooling channels in molds and dies inserts. To mitigate temperature variations, the objective function is penalized using the temperature standard deviation of the insert cavity surface. A density-gradient-based constraint is further utilized to reduce the generation of overhanging structures and promote manufacturability. In particular, the use of this constraint leads to the generation of channels characterized by a teardrop-shaped cross section. The cooling efficiency of a selected optimized design is confirmed through computations using a body-fitted solver. The geometry is subsequently manufactured by Laser Powder Bed Fusion (LPBF) and experiments are conducted to compare its performance in comparison to a design featuring straight-drilled channels. The results demonstrate that the optimized geometry significantly enhances the heat extraction rate and further leads to a 43% reduction of the cavity temperature standard deviation.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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