Aeroacoustic Process Monitoring and Anomaly Detection in Cold Spray Additive Manufacturing
Bibliographic record
Abstract
Abstract Cold spray (CS) is an emerging additive manufacturing method used to deposit a wide range of materials by spraying solid particles at supersonic velocities using high-pressure millimeter scale de Laval nozzles. As CS technology finds applications in diverse areas, including 3D printing, the need for in situ process monitoring becomes increasingly apparent. The CS process is influenced by various process parameters, including nozzle gas inlet pressure, temperature, and powder feed rate. Accurately detecting variations in these parameters, as well as identifying process anomalies (e.g., nozzle wear, clogging), is crucial for the broader implementation of the technology. In situ detection of anomalous events and process health monitoring is paramount for identification of inconsistencies, ensuring product quality, enhancing cost efficiency, and reducing waste by early detection of faults. To this end, in this study, airborne acoustic emission was monitored during CS processes to discern acoustically detectable process parameters. Characteristics of aeroacoustic waves emitted under both free jet and deposition conditions were analyzed. Results indicate that changes in nozzle gas inlet pressure and temperature, powder feed rate, and nozzle wear status are discernible through acoustic power spectrum analysis. Time-domain analysis further facilitated the identification of anomalies associated with powder injection termination, deposit/substrate delamination, and nozzle geometry changes. Notably, the sliding window first order backward differentiation of total power and the power band in the time domain proved effective in detecting gradual anomalies, such as nozzle throat wear, whereas the second-order differentiation highlighted abrupt process changes, like delamination. This study demonstrates that airborne acoustic signals offer valuable insights pertaining to process faults in CS, establishing aeroacoustic signal monitoring as a promising component of stand-alone or multi-modal process monitoring for CS operations. Furthermore, the study offers invaluable insights for aeroacoustic signal feature engineering for the development of machine learning models for process monitoring in CS.
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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 | 0.001 | 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 | 0.001 |
| 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 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".