Samuel von Baußnern

Abstract Medical device manufacturers face a critical challenge: how to scale production 5x while decoupling quality control costs from volume growth. Traditional sampling-based quality control - taking samples every four hours, with results arriving days to weeks later - cannot support this ambition. This talk shares our journey building a real-time ML quality control system that analyses every injection moulding shot in under one second, predicting part dimensions within 10μm accuracy. Core Theme: This session demonstrates how streaming data platforms transform traditional manufacturing quality control from reactive sampling to proactive, real-time decision-making. You'll see how we built a production-grade system that processes sensor data from injection moulding machines across global manufacturing sites, enabling immediate quality insights without touching the machines themselves. Technical Implementation: We built a hybrid on-premise and cloud architecture handling real-time sensor data streams. The system captures sensor data directly from the machines, sends them to the on-premise Kafka deployments, from where the ML models (deployed using Apache Flink) deliver predictions…