Heat transfer Simulation to determine the impact of Al-5Mg arc sprayed coating onto 7075 T6 alloy fatigue performance
Bibliographic record
Abstract
Aluminum-5% magnesium coatings was deposited by arc spraying onto aircraft Al 7075 T651 structural alloy for corrosion protection while required to maintain the substrate material fatigue performance integrity. Fatigue performance of coating system is complex and in order to better understand the variability of the fatigue performance of coatings, heat flow in substrate was studied and simulated to determine the temperature evolution during arc spraying in both substrate and coating for different process parameters. Experimental temperature measurements, theoretical calculations and simulation were carried out to extrapolate the coating temperature with respect to coating process variables and surface preparation. Both flux and conductance were identified by an inverse method to reproduce experimental temperature measurements. The thermal transient solver of LS-DYNA® was used to simulate the time-dependence of heat flux in the coating during successive depositions. The benefit of that model is its capability to predict the temperature distribution and evolution in time in a sample. Samples were made of Al 7075 T651 alloy and were 80 mm in length by 25 mm wide and 7 mm thick. A coating thickness of 250 μm was reached. It was the spray thermal energy that was taken into account in the model as the thermal load. The quality of the thermal contact between the substrate and coating was also included in the model and conductance was defined to control the amount of heat transferred at the interface. Coating performance was evaluated in term of fatigue properties, bond strength, and interface quality of as deposited coatings. The superior fatigue resistance of the coated alloy relies on low heat input process parameters and surface preparation that favor high interface conductance to keep low coating temperature during the coating process. Surface preparation, arc current and atomizing gases play all a key role to provide a fatigue resistant coating.
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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 | 0.000 | 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".