Powder characterization using X-ray tomography and image analysis
Bibliographic record
Abstract
Large scale deployment of additive manufacturing (AM) processes relies on part quality, specifically the presence of internai defects and part-to-part consistency. Sorne of the defects observed in finished parts have been associated with porosities in the powder feedstock used in AM processes including powder bed, laser cladding,and cold spray. Since the level of porosity in these powders is generally very low, standard characterization techniques, such as pycnometry and metallography with image analysis, are not well suited for quantification. This study presents a new approach combining high resolution X ray tomography with 3D image analysis to evaluate and quantify porosity in titanium powder feedstock. The effects of acquisition parameters and image analysis procedures on porosity quantification were investigated to validate the proposed method and assess its reliability. Data demonstrates that the proposed technique is sufficiently sensitive to differentiate powders with different porosity levels.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".