Production Yield Optimization (PYO) built on Project Bonsai: assessment and implementation
Production Yield Optimization built on Project Bonsai: assessment, simulation, training & edge IoT
Our application enables the development and deployment of a Project Bonsai AI agent for Production Yield Optimization (PYO) manufacturing scenarios.
The application is developed, trained, and deployed on the customer Azure subscription by Neal Analytics. It combines multiple elements that comprise the end-to-end Project Bonsai reinforcement learning process.
- Initial project assessment
- Machine teaching process
- Data preparation and integration (including necessary data platform cloud migration and/or modernization)
- If a process simulation is not available, Neal can also develop a custom simulation leveraging the most appropriate approach for the customer process: third-party simulation tools (e.g., AnyLogic, Simulink, etc.), custom physics-based models, custom digital twin-based simulations, or trained AI simulations.
- Bonsai Inkling training language definition
- AI agent training through Bonsai deep reinforcement learning platform
- AI agent testing (on simulations)
- Field deployment using Azure IoT Edge.
- Field testing on real-life process
- Reporting and monitoring
Typical manufacturing processes include (but are not limited to):
- Food, plastic, or metal extrusion
- Chemical or batch processes
- Discrete manufacturing processes
- Robotic operations
- Automatic equipment calibration (e.g., CNC)
- Manufacturing supply chain, warehousing, and inventory optimization
Each project is different and, depending on its complexity and stage different experts will be brought in throughout the 3-9 months initial project.
Please note that subscription-level access is required to deploy the solution.
This solution has been validated for Azure private MEC for edge connectivity and compute capability.