5 papers
Event-Driven ML Pipeline Orchestration for Manufacturing: An AWS Industry Experience
Zhengyang, Gu, Thomas Cook +3
We present an industry experience report on three years of operating an event-driven cloud infrastructure for continuous machine learning training in automotive manufacturing. Our…
A Kafka-Centric Communication Fabric for Near-Real-Time, Cloud-Replicated Closed-Loop Manufacturing Process Control
Zhengyang, Gu, John Burtenshaw +2
Smart manufacturing needs to move sensor data off the plant floor, react to it, and feed decisions back to actuators within bounded time. Programmable logic controllers (PLCs) hand…
Autonomous Model Lifecycle Management for Digital Twin-Based Manufacturing Control
Zhengyang, Gu, Thomas Cook +3
Manufacturing AI systems must autonomously adapt to continuous distributional shift from raw-material variability, ambient changes, and equipment aging, under strict safeguard and…
Augmenting PID Control with Deep Reinforcement Learning: A Hybrid Approach to the Industrial Benchmark
Zhengyang, Gu, Joseph E. Hernandez +4
As industrial processes grow in complexity, traditional Proportional-Integral-Derivative (PID) controllers are often insufficient for handling their non-linear, multi-input dynamic…
Benchmarking Hybrid Deep Learning Architectures for Predictive Maintenance in Industry 4.0
Zhengyang, Gu, Joseph E. Hernandez +4
Predictive maintenance in Industry 4.0 refers to using data from sensors, machines, and production systems to estimate when equipment is likely to fail, so maintenance can be plann…