Minimization of Cost and Print Time of Additive Manufacturing via Topology Optimization
Bibliographic record
Abstract
As the field of design for additive manufacturing continues to evolve and accelerate towards admitting more robust designs requiring fewer instances of user-intervention, we will see the conventional design cycle evolve dramatically. However, to fully take advantage of this emerging technology — particularly with respect to large scale manufacturing operations — considerations of productivity from a fiscal perspective are sure to become of the utmost importance. A mathematical model incorporating the cost and time factors associated with additive manufacturing processes has been developed and implemented as a multi-weighted single-objective topology optimization algorithm. The aforementioned factors have been identified as component surface area and volume of support material. These quantities are optimized alongside compliance, producing a design tool that gives the user the option to choose the relative weighting of performance over cost. In two academic examples, minimization of compliance alongside surface area and support structure volume yield geometries demonstrating that considerable decreases in support material in particular can be achieved without sacrificing significant part compliance.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".