Improved Process Controls of Combustion Sprayed Clearance Control Coatings Through Sensor Diagnostic Technology
Bibliographic record
Abstract
Abstract Sensor technology is becoming more of a production tool to help improve production quality, reliability and reduce manufacturing costs. Combustion sprayed abradable seal products are a family of materials where this technology will be helpful to the applicator and end user. Although these materials have been used for over forty years with wide success in the aerospace and industrial gas turbine industries they can be sensitive to spray process variables. Changes in spray processing conditions during spraying will change the desired microstructure and coating properties. This paper looks at a commercially available combustion powder and how process parameters such as gas flows and powder feed rates affect output process variables such as particle velocity and temperature. This paper will also discuss the importance of understanding the influences that particle temperature and velocity have on coating properties such as hardness, erosion and coating strength. Deposit efficiency of these combustion powders is also measured as a function of particle temperature and velocity. Based on particle temperature and velocity, sensor diagnostic tools can provide warnings about process changes resulting in fast corrective action. The benefits of this sensor technology are the potential for less inspection requirements, improved microstructure control, reduced in-service failures, and less time and labour required for stripping coated components that may not meet specification standards.
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) | 0.000 | 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".