Convolutional Neural Networks for Tribological Coatings

Sharmin Rahman, Zainab Das, Mateusz Mahmoud

Open Access Journal of Engineering Applications · doi:10.55555/oajea.2025.4902.004

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.

This full text was produced by the journal’s retrospective digitisation programme. Section headings follow the printed original.

2. Materials and Methods

The method is described in the printed article, of which the PDF galley is a faithful rendition.

3. Results and Discussion

Results are reported in full in the PDF, together with the figures and tables referenced above.

4. Conclusion

The reference list attached to this record is structured, and the article is deposited with a DOI so it can be cited exactly as it was in print.