Simultaneous topology and build orientation optimization for minimization of additive manufacturing cost and time
Bibliographic record
Abstract
Summary The ever‐present demand for increased performance in mechanical systems, and reduced cost and manufacturing time, has led to the adoption of computational design tools and innovative manufacturing methods. One such tool is topology optimization (TO), which often produces designs that are impracticable to manufacture. However, recent developments in additive manufacturing (AM) have made production of such complex designs feasible. Therefore, integration of these technologies has the potential to innovate the design and manufacture of mechanical components. This work presents a novel mathematical methodology for multiobjective minimization of structural compliance and AM cost and time, in simultaneous build orientation and density‐based TO. Component surface area and support volume were implemented in this method as the physical factors influencing AM cost and time. A new methodology was produced to approximate support volume throughout TO with variable build orientation, enabling direct minimization of support volume in the proposed optimization. The methodology allows derivation of sensitivity expressions, thereby permitting the use of efficient gradient‐based optimization solvers. Three numerical examples demonstrated that the proposed methodology can efficiently produce optimum build orientations and topologies, which significantly reduce structural compliance and AM cost and time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".