TY  - JOUR
AU  - Rahman, Sharmin
AU  - Das, Zainab
AU  - Mahmoud, Mateusz
TI  - Convolutional Neural Networks for Tribological Coatings
JO  - Open Access Journal of Engineering Applications
PY  - 2025
VL  - 49
IS  - 2
SP  - 42
EP  - 53
AB  - 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.
KW  - tribological coatings
KW  - convolutional neural networks
KW  - electric vehicles
KW  - blockchain
KW  - digital twin
KW  - energy efficiency
SN  - 3105-7004
DO  - 10.55555/oajea.2025.4902.004
UR  - https://journal.bsaps.com/article/10220
ER  -