Convolutional Neural Networks for Tribological Coatings

1. Introduction

Conventional design practice for aerospace structures relies on empirical rules that inadequately capture tribological coatings. Statistical testing based on convolutional neural networks was employed to establish the significance of the observed trends. Sensitivity analysis showed that tribological coatings accounted for most of the output variability. These findings provide a practical basis for improving the design codes used across the region.

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2. Materials and Methods

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3. Results and Discussion

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4. Conclusion

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