Numerical analysis of thermal spray coatings using artificial neural networks (ANN) overview

Numerical analysis is vital in studying and optimizing surface coating processes, which involve applying a thin layer of material onto a substrate to improve properties like wear resistance, erosion, oxidation, corrosion resistance, and aesthetics. To optimise the coating deposition process, parametric optimisation and process modelling are necessities. Numerical analytical techniques are employed to investigate the quantitative disparity between a process's inputs and outputs. To effectively reduce the quantitative difference between process inputs and outputs and enhance their link, machine learning approaches are currently taking the role of statistical procedures. Introducing artificial neural networks (ANNs) and their application to surface coating process modelling and parameter optimization is the main goal of this review. It implies that neural networks can lessen expenses, increase surface engineering, and improve performance of thermal spray coatings. This study reviews HVOF thermal spray, plasma spray, and flame spray processes that are optimized using numerical analysis.