Stefan Schrunner, Michael Scheiber, Anna Jenul et al.
Since high data volume and complex data formats delivered in modern high-end production environments go beyond the scope of classical process control systems, more advanced tools involving machine learning are required to reliably recognize failure patterns. However, currently, such systems lack a general setup and are only available as application-specific solutions. We propose a process control framework entitled Health Factor for Process Control (HFPC) to bridge the gap between conventional statistical tools and novel machine learning (ML) algorithms. HFPC comprises two main concepts: (a) pattern type to account for qualitative characteristics (error patterns) and (b) intensity to quantify the level of a deviation. While the system retains large model generality, allowing a broad scope of potential application areas, we demonstrate its favorable mathematical properties in a theoretical analysis. In a case study from the semiconductor industry, we underline that (a) our framework is of practical relevance and goes beyond conventional process control, and (b) achieves high-quality experimental results. We conclude that our work contributes to the integration of ML in real-world process control and paves the way to automated decision support in manufacturing.