Work

From lab to plant: Data-driven modelling of an electroplating process

Year: 2026

Type: article

Abstract: Electroplated zinc-nickel (Zn-Ni) coatings are commonly applied as corrosion protection for steel components in demanding industrial environments. However, due to high process complexity, coupled with a limited process transparency and irregular chemical monitoring, achieving resource efficiency and predictive process control still has its challenges. This paper presents a data-driven approach for monitoring and modelling of an alkaline Zn-Ni barrel plating process at process and product level using machine learning (ML) techniques. A methodology was developed within a cyber-physical production system framework for training the models based on a controlled laboratory environment (20 L bath volume) and knowledge transfer on an industrial electroplating plant (13,000 L bath volume). Different supervised algorithms were trained with a limited set of input features to predict metal concentrations of the bath, coating thickness, nickel ratio in the coating and aggregated coating quality indicator. Despite data scarcity and process variability, the selected best models demonstrate accurate prediction rates (> 86%). The results show that experimentally generated data can improve product-level modelling in the industrial environment, while process-level transfer remains limited when relevant industrial interventions, such as zinc dosing, are not recorded.

Source: Advances in Industrial and Manufacturing Engineering

DOI: 10.1016/j.aime.2026.100194

Authors: Marija Lindner, Rowena Duckstein, Christoph Herrmann

Institutions: Technische Universität Braunschweig, Fraunhofer Institute for Surface Engineering and Thin Films

Language: English

Topic: Electrodeposition and Electroless Coatings

Subfield: Surfaces, Coatings and Films

Field: Materials Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,300