Manufacturing optimization of laminated tooling with conformal cooling channels
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This paper proposes sheet thickness determination in manufacturing of laminated dies as an optimization problem. The aim of this optimization procedure is finding the best set of thicknesses which minimizes the volume deviation between actual computer‐aided design (CAD) model and assembled slices. Design/methodology/approach This works uses a modified version of genetic algorithms for the optimization purpose. Each set of thicknesses that can cover the whole CAD model surface is considered as a chromosome. Genetic operators such as crossover and mutation have to be modified to be used in this application. Findings A new method for finding the total volume deviation between assembled slices and the actual CAD model was developed in this research. On the other hand, the results show how the program can automate the slice plane locations search process. Research limitations/implications Premature convergence does not allow the algorithm to search the entire solution space before getting trapped in a local optimum. Even the mutation operator cannot postpone this untimely convergence. Practical implications The proposed method is a good substitute for the manual methods that are currently used in industry. These experience‐based methods are mostly based on the decision made by a well‐trained technician on picking up the thicknesses for a specific CAD model. Originality/value This is the first attempt at optimizing the slicing method in laminated tooling. Other methods are mostly based on rapid prototyping (RP) and they are not applicable in the laminated tooling process since, despite RP, here not all optimization outputs can be used in practical procedure.
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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