A static and high-cycle fatigue characterization framework of metallic lattice structures additive manufactured via fused deposition modeling based method
Bibliographic record
Abstract
• Metal fused deposition modeling additively manufactured multiscale lattice structures. • A framework characterizes static / high-cycle fatigue properties of printed lattices. • The framework employs Asymptotic Homogenization and Brown-Miller-Morrow methods. • The printed lattice microstructures are quasi-brittle with a deteriorated stiffness. • Residual pores initiate microcracks, deteriorating lattices’ high-cycle fatigue life. Compared to conventional metal additive manufacturing techniques, metal fused deposition modeling (Metal FDM) reduces cost at the expense of deterioration in materials’ mechanical performance. To realize the full design potential that Metal FDM components can offer, effectively predicting the performance becomes imperative, especially for lattice structures that are widely used in aerospace under complex and cyclic loading. This work developed a framework for characterizing and predicting static and high-cycle fatigue behaviors of FDM-printed metal lattices. Constitutive model constants of FDM-printed 17-4PH steels were identified via experiments on dog bone samples at the same length scale of lattice microstructures. The material exhibits quasi-brittle behavior at microstructural size, with a tensile stiffness of 24 GPa. It is only 13 % of the expected stiffness for macroscopic level materials, showing a severe effect by length scale. Residual porosity leads to microcracks, which act as the primary failure mechanism under high-cycle fatigue, reducing the fatigue limit to 31 % of rolled steel. Assigning developed constitutive models, the asymptotic homogenization method was employed to obtain equivalent static properties of stretch- and bend-dominated lattices, which were in accord with testing results. Through the Brown-Miller-Morrow method, the framework numerically predicted lattice high-cycle fatigue life, which was validated against experiments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".