Artificial intelligence-based model for physical-mechanical surface properties of nanostructured coatings

Abstract

This article presents a computational numerical model for the simulation and analysis of quantum chemistry and Gibbs free energy theory using static (ANNS), dynamic (DANN), and chaotic neural networks (CANN). The model calculates the physical-surface mechanics of hardness, adhesion, and strength. They resulted in nanostructured metal coatings with electrodeposited chromium nanoparticles on low-carbon steel. The ANNS, DANN, and CANN simulations showed that model-obtained values for analyzed properties presented an approximation of 99 % concerning theoretical matters taken as base. Likewise, model accuracy was verified by comparison with reference data (datasheet). The proposed model is not limited to the analyzed case and provides consistent results for predicting surface physical-mechanical properties of electrodeposited coating-substrate arrangements, with a minimum error percentage of 1–1.5 % over learning.

Keywords

• Artificial intelligence
• Quantum chemistry
• Physical-mechanical surface properties
• Nanostructured metallic coating