Digital Transformation through Artificial Intelligence, Machine Learning, Simulation, and Data Science
This session explores the transformative role of digital technologies in the domain of industrial thin film deposition, particularly within vacuum-based coating technologies. The focus is on leveraging physics-informed simulation, artificial intelligence, and data-driven methods to enhance process understanding, optimization, and control.
The session will include, but is not limited to, the following topics:
- Physics and Chemistry Simulations: Use of high-fidelity, multi-physics models to predict key process parameters like erosion and deposition profiles, film composition, ion bombardment, gas and plasma distributions, and substrate heating.
- Digital Twin Models: Real-time capable simulations that integrate equipment layout and operating parameters to forecast coating performance and variability.
- Machine Learning and Hybrid Approaches: Applications of AI for predictive maintenance, parameter tuning, and anomaly detection. Emphasis on combining limited experimental data with physical modeling for higher generalizability (greybox models).
- Data Infrastructure and Process Mining: Tools and methods for systematic data acquisition, storage, accessibility, and intelligent analysis across the coating process chain.
This session is intended for all stakeholders involved in the digital transformation: OEMs and system integrators in the vacuum coating sector, coating service providers and production engineers, developers of coater components and diagnostic tools, as well as providers of simulation software and digital services, and data mining platform providers.
Digital Transformation of Industrial Deposition Processes TAC Co-Chairs: Holger Gerdes, Fraunhofer-IST, holger.gerdes@ist.fraunhofer.de; Adam Obrusnik, PlasmaSolve s.r.o., obrusnik@plasmasolve.com; Paul Nizenkov, boltzplatz – numerical plasma dynamics GmbH, nizenkov@boltzplatz.eu