Machine Learning for Competitive Grain Growth Behavior in Additive Manufacturing Ti6Al4V
Why this work is in the frame
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Bibliographic record
Abstract
Metal additive manufacturing (MAM) technology is now changing the pattern of the high-end manufacturing industry, among which MAM fabricated Ti6Al4V has been far the most extensively investigated material and attracts a lot of research interests. This work established a deep neural network (DNN) to investigate the grain boundary in competitive grain growth for a bi-crystal system, the column β grains of Ti6Al4V as an example. Because of the limited number of experimental samples, the DNN is trained based on the data coming from the Geometrical Limited criterion. A series of direct energy deposition experiment using Ti6Al4V is carried out under the Taguchi experimental design. The grain boundary angles between the column grains are measured in the experiment and used to evaluate the accuracy of DNN.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it