Particle velocity detection in laser deposition processing
Bibliographic record
Abstract
Purpose Particle velocity is a critical factor that can affect the deposition quality in manufacturing processes involving the use of a laser source and a powder‐particle delivery nozzle. The purpose of this paper is to propose a method to detect the speed and trajectory of particles during a laser deposition process. Design/methodology/approach A low‐power laser light sheet technique is used to illuminate particles emerging from a custom designed powder delivery nozzle. Light scattered by the particles is detected by a high‐speed camera. Image processing on the acquired images was performed using both edge detection and Hough transform algorithms. Findings The experimental data were analyzed and used to estimate particle velocity, trajectory and the velocity profile at the nozzle exit. The results have demonstrated that the particle trajectory remains linear between the nozzle exit and the deposition plate and that the particle velocity can be considered a constant. Originality/value The use of low‐power laser light sheet illumination facilitates the detection of isolated particle streaks even in high‐powder flow rate condition. Identification of particle streaks in three subsequent images ensures that particle velocity vectors are in the plane of illumination, and also offers the potential to evaluate in a single measurement both velocity and particle size based on the observed scattered characteristics. The method provides a useful simple tool to investigate particle dynamics in a rapid prototyping application as well as other research fields involving the use of powder delivery nozzles.
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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) | 0.000 | 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".