The SmARtPlaS project, funded by the German Federal Ministry of Education and Research from October 2019 to March 2023, set out to embed Industry 4.0 concepts into galvanotechnic production. Eight partners – the Institute for Occupational Science and Technology Management at the University of Stuttgart, DiTEC Dr. Siegfried Kahlich & Dierk Langer GmbH, the Institute for Machine Tools and Manufacturing Technology at TU Braunschweig, Softec AG, B+T Oberflächentechnik GmbH, Fraunhofer IPA, Airtec Mueku GmbH, and Nova Measuring Instruments GmbH – collaborated to create a cyber‑physical production system that couples a digital twin of the entire galvanic process chain with real‑time data from plant sensors, ERP software and plant control units.
The core technical achievement is a fully dimensional, scalable 3‑D digital twin that represents every stage of the electrochemical coating process, the coating plant itself, and peripheral subsystems such as exhaust, cooling, heat‑recovery and wastewater treatment. By feeding sensor data into the twin, the system can simulate electrolyte concentrations on the fly, enabling precise dosing strategies that were previously based on static tables. The integration of ERP data allows the twin to reflect production schedules and material inventories, so that the plant can be optimised holistically rather than in isolated subsystems.
From the digital twin, a modular service platform was derived. Each module – for the coating process, the coating plant, peripheral systems, and overall plant management – can be deployed as a stand‑alone solution or combined into a full suite. The modules communicate through a common data layer, so that state information from one component automatically informs the others. Predictive maintenance is realised by continuously monitoring key parameters; when a deviation is detected, the system can trigger an alarm or recommend a corrective action. The platform also delivers actionable information to operators via augmented‑ and virtual‑reality interfaces, simplifying complex data into intuitive visualisations that fit into existing workflows.
Performance results reported in the project documents show that the simulation‑based concentration determination reduces electrolyte waste by up to 15 % compared with conventional dosing, while maintaining coating quality. The predictive maintenance module cut unplanned downtime by roughly 20 % in pilot installations, and the AR/VR training tool lowered onboarding time for new operators by about 30 %. These figures demonstrate that the digital twin and its associated services not only improve resource utilisation but also enhance plant reliability and workforce efficiency.
The collaboration structure was organised around clear roles: the University of Stuttgart led the research on digital twin modelling and data integration; DiTEC and Airtec Mueku supplied industrial expertise on coating equipment and process control; Softec and B+T Oberflächentechnik focused on peripheral system optimisation; Fraunhofer IPA handled the development of predictive maintenance algorithms; and Nova Measuring Instruments provided measurement hardware and software. The project’s timeline was divided into four phases – concept development, prototype implementation, pilot testing, and final evaluation – each with defined deliverables and milestones.
In summary, SmARtPlaS delivered a comprehensive, modular digital‑twin‑based platform that unifies galvanic process data, ERP information and plant control into a single cyber‑physical system. The resulting improvements in dosing accuracy, resource utilisation, maintenance efficiency and operator training illustrate the tangible benefits of Industry 4.0 integration in the galvanotechnic sector. The project’s modular design and proven performance metrics make it readily transferable to other coating plants, positioning SmARtPlaS as a model for digital transformation in industrial surface finishing.