A method to assess the quality of additive manufacturing metal powders using the triboelectric charging concept
Bibliographic record
Abstract
A new method to assess the quality of additive manufacturing (AM) metal powders using the triboelectric charging concept is demonstrated using CpTi, Ti6Al4V, AlSi10Mg, IN 738, and SS 316L powders. For each powder tested, the surface chemical composition was first analyzed using X-ray photoelectron spectroscopy (XPS) to determine the composition of the passivation layer. Some modifications to the current GranuCharge™ setup, developed by GranuTools™, were then performed by incorporating a flow rate measuring tool to assess how tribocharging is affected as a function of flow rate. Variations in the tribocharging response have been found with the flow rate of CpTi, AlSi10Mg and SS 316L powders. Moreover, results suggest that the tribocharging behavior might not be the same even with powders fabricated with the same passivation process. Finally, the compressed exponential model of Trachenko and Zaccone was used to reproduce the tribocharging behavior of the powders. The models were found to work best when the stretch constant β = 1.5, which is identical to the value found in other systems such as structural glasses, colloidal gels, entangled polymers, and supercooled liquids, which experience jamming when motion of individual particles become restricted, causing their motion to slow down.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".