Classification of Particle Shape Using Two-Dimensional Image Analysis
Bibliographic record
Abstract
With the constant evolution of additive manufacturing (AM) processes, there is a need to adapt current characterization methods to better understand metallic powder behavior. Accurate and quantifiable characterization of powder particles is essential for qualification, certification, and quality control of AM manufactured parts. Particle morphology is often stated as an important parameter that affects powder flowability, layer density/uniformity, and—ultimately—part quality. However, work still needs to be accomplished to correlate particle characteristics to their impact on AM processes and manufactured parts. This study presents the sensitivity of various shape descriptors used in two-dimensional image analysis to particle morphologies commonly observed in AM. The objective was to determine which standard descriptors could adequately differentiate powder characteristic features such as elongation, facets, number, and size of satellites. To do so, a library of schematized particles containing various shapes was used and a sequential methodology capable of adequately classifying and quantifying particle shapes was developed. The methodology was then validated on metallographic cross sections of powders. The proposed approach could serve as a guide when selecting the most appropriate shape descriptors to monitor various powder characteristics and also provide a more complete characterization of particle morphologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.042 | 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".