From Weeks to Seconds: Real-Time ML Quality Control for Medical Device Manufacturing

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 to shop floor operators in under one second end-to-end. I'll share our architectural decisions, the challenges of maintaining sub-second latency at scale, and how we validated ML model accuracy against precision measurement equipment.

The Journey & Key Learnings: Rather than presenting a polished success story, I'll walk through our iterative hypothesis-testing approach—what worked, what failed, and why. I'll discuss the human factors: building trust through transparency, involving shop floor workers in the design process, and navigating medical device manufacturing regulations.

Audience Takeaways: Attendees will learn practical patterns for implementing real-time ML in industrial environments, strategies for iterative validation of streaming system assumptions, and how to bridge the gap between data science prototypes and production-grade systems that non-technical users trust and adopt.