Concurrent print orientation and topology optimization for fiber reinforced additive manufacturing considering mass minimization and compliance minimization problems statements
Bibliographic record
Abstract
Fiber reinforced additive manufacturing (FRAM) combines the benefits of composite materials and additive manufacturing to create components which are made of high-performance materials, have complex geometry, and are highly configurable to address a design objective. As such, FRAM components are perfect candidates for numerical optimization methods including fiber orientation optimization and topology optimization. Many methods optimize fiber orientation and topology parallel to the print plane and limit the available design freedom by constraining the solutions to exist only within a user-defined print-plane(s). This work proposes a numerical optimization method for FRAM which concurrently optimizes 3D print orientation $$(\theta_{1} ,\theta_{2} ,\theta_{3} )$$ , and component topology, (ρ). Print orientation design variables establish a domain-level, 3D orientation of FRAM print-plane and fiber orientation. The print orientation represents a diverse configuration of anisotropic material properties which improves a structural objective function. Optimized anisotropic material properties are unique to component loading, geometry, and problem statement. Topology optimization alters material distribution within an anisotropic state to improve the common objective function and allows integration of mass minimization problem statements. The method is applied to complex, industry-level examples and is used to solve compliance minimization and mass minimization problem statements. Optimized designs are compared to equivalent-mass metallic and conventional FRAM designs. Structural compliance of an aircraft seat component is improved by 38.4% compared to an equal-mass aluminum design. The mass of a mounting bracket is reduced by 51% compared to an equal-displacement aluminum design.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".