Webinar recording: Optimizing process manufacturing with Artificial Intelligence
Process manufacturing from extrusion to batch, paper, fiber, and chemical processes are complex and hard to optimize. Unfortunately, traditional command and control systems have limited capabilities in reducing their start-up cost, limiting specification drift, or ensuring consistent quality control.
With the advent of intelligent IoT sensors and actuators, Industry 4.0 opens the door to productivity advances not possible yet.
However, to fully reap the benefits of Industry 4.0, the command and control paradigm also needs to improve. With Autonomous Systems, manufacturers can now develop, train, and deploy advanced AI Agents on the shop floor, even without large data scientist teams.
This webinar will share how Microsoft Project Bonsai Deep Reinforcement Learning helps bring AI from the research labs to the manufacturing floor effectively and efficiently.
- Where can AI agents help optimize process manufacturing?
- What is Deep Reinforcement Learning (DRL) for AI agents’ training?
- How to build an AI agent with Microsoft Project Bonsai toolchain
- Demo: Plastic extrusion optimization
- Case study: PepsiCo’s Cheetos extrusion optimization with Bonsai
Watch the webinar recording
- About Autonomous Systems
- Check these 5 points to consider before starting your deep reinforcement learning autonomous system project in process manufacturing optimization
- Learn how to improve process manufacturing productivity with AI
- Learn how to improve the system-level efficiency of your extruder using AI
- Learn how to improve specification drift robustness using AI
- Check out our step-by-step guide to Project Bonsai