Thermal Spray Coating on Polymeric Composite for De-Icing and Anti-Icing Applications
Bibliographic record
Abstract
Abstract This paper reports a novel method for fabrication of an electro-thermal heating element, as de-icer or anti-icer, for the polymer-based composites. The plasma spray technique was utilized for the deposition of a Nickel-Chrome-Aluminum-Yttrium (NiCrAlY) coating layer as a heating element on top of the glass/epoxy composite. To improve the adhesion strength and deposition efficiency of the coatings and to protect the composite fibers during grit blasting and spraying, a woven wire stainless steel mesh was added to the composite substrates during the composite fabrication process. Metal mesh will act as an anchor to keep the coating on the surface. Two types of woven wire and two types of NiCrAlY powder with the fine and coarse particle size distributions were used. Good processing parameters for grit blasting and plasma spraying are identified. It is found that the surface modification method applied to the composite substrates prior to the coating process makes a significant improvement in the coating thickness uniformity and deposition efficiency. Several tests were conducted on the coated samples for determination of their mechanical and electrical properties. Using flatwise tensile tests, it is shown that application of proper surface modification method and set of spray parameters could result in improving the coating bonding strength significantly. The electrical and thermal analyses of the coated samples are also performed. It is shown that the coated samples have a high capability in the generation of heat and can be used as a heating element.
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 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 | 0.000 |
| 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) | 0.001 | 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".