A modular AI-powered digital twin framework designed to revolutionize food manufacturing by integrating physics-based simulations, real-time sensor data, and machine learning for enhanced process control and reduced trial-and-error.
The high-fidelity digital twin framework is an advanced solution aimed at transforming food and consumer goods manufacturing. This AI-powered system integrates physics-based simulations, real-time sensor data, and machine learning models to provide predictive and adaptive process control. By utilizing virtual sensors and a modular design, the framework reduces reliance on trial-and-error methods, accelerates development, and minimizes operational costs. Its plug-and-play architecture ensures easy adoption and compatibility with existing workflows, making it suitable for R&D teams with varying levels of expertise.
Key features include:
This framework allows for rapid experimentation and design iterations, supporting tailored adaptation without disrupting established procedures.
The technology is at TRL 5, indicating that it has been validated in a relevant environment. Future validation plans include a 12-18 month development cycle focusing on problem identification, model selection, real-time testing, and beta testing with feedback integration.
Washington State University is a comprehensive public land‑grant research university serving the state through a multi‑campus system anchored in Pullman. Industry engagement runs through a research and technology park in Pullman, a health sciences campus in Spokane integrated with regional hospitals, and a statewide extension network linking faculty with companies and communities. The Tri‑Cities campus sits minutes from a U.S. Department of Energy national lab, enabling collaborations, shared facilities access, and talent pipelines for regulated sectors. Research is supported by competitive federal funding from agencies such as NSF, NIH, USDA, DOE, and DoD. A dedicated technology transfer office manages IP, licensing, and startup support, complemented by incubator and pilot‑scale resources.