Properties and applications of additively manufactured metallic cellular materials: A review
Bibliographic record
Abstract
Additive manufacturing (AM) refers to a collection of manufacturing methods involving the incremental addition of material to build a part directly in its final or near-final geometry, usually in a layer-by-layer process. Metal AM in particular has seen great industrial adoption and maturation. This technology enables increased freedom of design in engineered materials with complex geometries, of which architected cellular or lattice structures are particularly promising in a wide range of applications. These materials are similar to stochastic foams which have found many industrial applications over the last few decades, but regular cellular structures possess a higher degree of control over the manufactured architectures made possible by additive manufacturing. These architected porous materials have properties that can be fine-tuned for a particular application (for mechanical performance, permeability, thermal properties, etc.). The control over the design and manufacturing of such architected structures in comparison to stochastic structures opens new application possibilities and enables a range of new products and features. This potential is only starting to be realized as metal AM techniques are maturing and are increasingly being adopted in various industries, and as design-for-AM capabilities improve. This review paper summarizes the unique properties of AM lattice structures and how these have been successfully employed for specific applications so far, and highlights various application areas of potential interest for the near future. The focus in this review paper is therefore on unique achievable properties and the associated applications for metal additively manufactured lattice structures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.004 | 0.002 |
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".