Virtual Testing of Synthetic Polycrystal Microstructures Predicting Elastic Properties of Additive Manufactured Alloy 718
Bibliographic record
Abstract
Additive manufacturing (AM) is gaining significant attention in manufacturing engineering owing to its advantages compared to traditional manufacturing methods.Microstructures that result from the AM process often lead to anisotropic mechanical properties of produced components.In this study the Ni-based Alloy 718 is analysed.It has been shown that the microstructure of this polycrystalline material can be tailored to obtain different grain morphology distributions and crystallographic textures.In this paper, the reproduction of three typical microstructures, equiaxed, columnar and combined (equiaxed and columnar), are investigated to determine their elastic anisotropic properties.Virtual testing is applied on synthetic representative volume elements (RVE) for the equiaxed and columnar grain structures, and representative area element (RAE) for the combined structure.The crystal elasticity finite element method (CEFEM) is utilized to predict macroscopic elastic properties.This method allows the implementation of grain crystallographic orientations as input texture and the generation of homogenized elastic stiffness matrix predicting the directional engineering stresses of polycrystal microstructures.The comparison of the simulation results for the three microstructures studied demonstrates significant property variation.Also, the comparison of the different number of grains and various interface area cases of the combined structure shows diversity in the results presented in this study.
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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) | 0.000 | 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 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".