Cold spray onto carbon fibre reinforced polymer for lightning strike protection
Bibliographic record
Abstract
Carbon fibre reinforced polymer (CFRP) is a very competitive alternative to aluminum for aircraft structures for lightweighting purposes, but this leaves vulnerability against lightning strike. Cold spray is one coating approach to make the polymers lightning strike proof. The aim of this work is to use cold spray on aircraft quality CFRPs to produce a metal coating with sound mechanical properties and good electrical conductivity for lightning strike protection. Copper, aluminum and tin were used as the coating materials and cold sprayed with both a high-pressure and a low-pressure cold spray system. A number of different combinations of the gas pressure and gas preheating temperature were used for the cold spray process. Erosion was found to be the key obstacle to developing continuous coatings on the CFRP substrates with the high-pressure system. On the other hand, continuous tin coatings were successfully obtained on the CFRP with the low-pressure system, due to the very soft tin coating the substrate through a "crack filling" mechanism; a process map was generated to define appropriate cold spray conditions. The tin/CFRP adhesion, which was assessed by pull-off tests, revealed the bonding was mainly mechanical since the adhesion strength was at a low level. Copper, aluminum and zinc powders were mixed with tin with the aim of increasing conductivity, and the addition of copper and zinc led to much higher deposition efficiencies compared to the pure tin coating. To understand, more generally, the cold sprayability of metal powders on polymeric substrates, powders of various compositions and characteristics were sprayed on different polymer substrates. Thermoplastic polymers generally showed positive results; in particular, thick copper coatings were successfully deposited on two thermoplastics. The electrical conductivity of the cold sprayed coatings was measured by the four-point conductivity measurement method. The conductivity in the as-sprayed tin coating was approximately half of that of bulk tin, and increased to 73 per cent as conductive as bulk tin after annealed at 80°C for 72 hours. The addition of copper generally did not increase the overall conductivity due to the growth of the more resistive intermetallics. Last but not least, continuing current injection tests, which duplicated component C of a lightning waveform, were performed on the tin coatings. The results showed that the cold sprayed tin coatings can provide effective protection to the CFRP underneath when subjected to a 100A current.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".