Probabilistic graphical models for diagnosing defectivity patterns

Abstract

In the high-tech sector, diagnosing performance issues often involves analysing a variety of defectivity patterns on products. High-tech systems perform numerous processes – e.g. product handling, light projection, jetted ink application, and thermal treatments – all of which affect the quality of the product itself. Their potential malfunctioning can contribute to defects with characteristic patterns. Often there is not a one-to-one mapping between root causes of these malfunctions and the resulting observable defectivity patterns. Consequently, identifying the root cause of these patterns is a challenging and an intrinsically probabilistic task. This paper proposes a framework to relate these patterns to the underlying root causes and employs Probabilistic Graphical Models (PGM) to reason about these relations. We find that PGMs ability to contain arbitrary graph topologies and jointly reason across all root causes empowers the modeller to adapt the models to the system at hand and include domain specific knowledge that would be hard to account for using more data-driven approaches. When provided with data from an operational system in the field, the PGM identifies the underlying root causes of product quality issues. We demonstrate the methodology with a real use case from the production printing industry.