4 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…
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…