Automated Optimal Design for Manufacturability of Sheet/Plate Assemblies
Bibliographic record
Abstract
A wide variety of products are manufactured from raw materials that are in the form of sheets or plates. Once the product is designed, parts are unfolded or flattened into flat blanks, which are nested onto the raw material for cutting. Optimization of nesting and packing problems has been an active research field for many years, and many good algorithms have been created. These algorithms have a fundamental limitation, however, in that they assume the set of blanks to be nested is fixed. In this work we relax this assumption, and by linking a parametric CAD system, a part-unfolding module and a sheet-nesting module that all intercommunicate, nests are created which maintain the parametric dimensions of the assembled product. Given a nest of the set of required blanks, dimensions of the blanks are optimized for a particular objective, such as maximizing raw material utilization or minimizing total use of raw material, subject to assembly, part dimension, part and blank dimension constraints. Once optimized, these blank dimensions are returned to the CAD system to update the product model. Through the use of this system, a designer can simultaneously optimize all the dimensions within a product to minimize manufacturing costs early in the design phase while maintaining acceptable product performance. This paper will demonstrate a prototype of this DFM system, discuss issues such as performance improvement through randomized trials, and suggest how additional design objectives (e.g., strength to weight ratio, stiffness, etc.) can be integrated with the reduced manufacturing cost objective.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".