Additive manufacturing powder feedstock characterization using X-ray tomography
Bibliographic record
Abstract
To answer the need for efficient quality control protocols for additive manufacturing processes and materials, specific testing methods for powder feedstocks should be developed. A powder feedstock may contain some defects, such as porosities, that will remain in the final parts after the building process. X-ray tomography combined with 3D image analysis offers unique advantages over other characterization methods, such as pycnometry and metallography, in respect to quantifying internal porosity in the individual particles of the feedstock. This paper presents the effect of X-ray tomography parameters on the quality of the obtained images and its impact on the image analysis. An automated image analysis routine was also developed to allow the visualization of the pores inside the particles but also, more importantly, to quantify this internal porosity contents, as well as to provide information on the morphological features of these pores, such a size distribution, number of particles containing pores and the volume fraction of a pore inside a particle.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".