Quantification of metal powder contamination and variation of properties during multi-usage binder jetting process
Bibliographic record
Abstract
During binder jetting additive manufacturing (BJ-AM) processing and powder re-use treatment, changes to the powder surface properties leading to degradation of certain characteristics may not be detected by traditional powder characterization techniques. In addition, in BJ-AM, the inherent poor flowability of powders ascribed to their fine particle size distribution, makes the traditional powder characterization methods, such as the measurement of static and dynamic flowability, less efficient to detect the presence of surface contaminant or a given level of surface degradation or even both phenomena. In this paper a method for quantifying powder contamination is proposed based on the triboelectric charging using the GranuChargeTM apparatus. Using the compressed exponential model proposed in Galindo et al., triboelectric constants, Qe and α, are calculated and then used to obtain the “n” constant or charging rate. Values of n are reported to be 0.38, between 0.44 and 0.39, and 0.59 for Cu, LSA and SS316L powders in the AR condition, respectively, and the values increased for the three powders due to the presence of binder residues and oxide species on the surface of the powders upon reuse. The results were compared with those obtained from traditional powder characterization techniques, including particle size analysis, XRD, Hall and Carney funnels, GranuDrumTM, and XPS. The method was demonstrated using three families of AM powders used in BJ: copper, low steel alloy, and stainless steel 316 L.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".