AI‑driven process‑control for autonomous vacuum metallizing

Presented by Chris Marshall, BOBST North America

The increasing complexity of vacuum metallizing processes, combined with rising quality expectations and a global shortage of skilled operators, has accelerated the need for intelligent automation in film converting. This paper presents the **Intelligent Metallizing Assistant (iMA)**, an AI‑driven process‑control and decision‑support system developed for next‑generation vacuum metallizers. Integrated into our metallizing platform, iMA leverages machine learning, advanced sensors, and embedded process expertise to automate critical metallizing operations traditionally dependent on operator experience.   iMA continuously monitors key process variables and applies predictive algorithms to optimize machine settings in real time. By automating setup procedures, stabilizing thermal and deposition conditions, and providing adaptive operator guidance, the system significantly reduces process variability and shortens changeover times. Experimental results from industrial production environments demonstrate improvements in metallizing uniformity, reduced energy consumption, and measurable decreases in waste and downtime.   The paper details the system architecture, including sensor integration, data‑driven control loops, and the human–machine interface designed to support operators with varying skill levels. It also discusses the role of AI in codifying metallizing know‑how, enabling consistent production quality across shifts and facilities. The Intelligent Metallizing Assistant represents a major step toward autonomous metallizing, offering a scalable pathway to higher productivity, improved sustainability, and more resilient manufacturing operations.