Application of artificial neural networks in predicting and optimising electroplating parameters: A systematic review

¹ Next Frontiers in Advanced Materials Laboratory, Department of Chemical and Metallurgical Engineering, University of the Witwatersrand, Private Bag 3, Wits, 2050, Johannesburg, 2000, South Africa
² Advanced Material Division, Mintek, Private Bag X 3015, Randburg, 2125, South Africa

^* Corresponding author: nomalu0803@gmail.com

Abstract

Optimising electroplating parameters is crucial for enhancing coating performance, but traditional methods are often time consuming and resource intensive. This review evaluates the application of Artificial Neural Networks in electroplating optimisation, highlighting their predictive accuracy and efficiency. A VOSviewer analysis explores how the application of artificial neural networks in electroplating has evolved, from advancements driven by the Fourth Industrial Revolution to emerging uses aimed at mitigating climate change. Findings show that hybrid and ensemble ANN approaches with Statistical Regression, Random Forest, and Support Vector Machine achieve R² values above 0.92, outperforming traditional techniques. However, data scarcity, model interpretability, and computational demands remain a challenge. Addressing these limitations through improved data availability and AI integration can enhance ANN adoption in industrial workflows, promoting more efficient and sustainable electroplating processes.