Data-driven digital twin framework for adaptive process optimization

Overview

Our data-driven digital twin framework is a cutting-edge solution designed to optimize industrial processes by integrating real-time sensor data with physics-based simulations. This statistical-AI approach enhances traditional digital twins, allowing for improved yield prediction, reduced trial-and-error, and accelerated optimization. The framework has demonstrated success in manufacturing environments, where it not only improved yield prediction but also increased design efficiency. It functions as a fast, interpretable decision layer that engineers can leverage to make informed decisions, thereby embedding AI tools into design pipelines for better process outcomes.

Technical specifications

• Statistical-AI framework: Combines statistical learning with physics-based simulations to predict performance, quantify uncertainty, and detect drift.
• Real-time data integration: Utilizes real-time plant data to inform simulations, capturing real process variability.
• Surrogate models: Builds surrogate and calibration models that reduce computation time while maintaining accuracy.
• Proven application: Successfully implemented in manufacturing to enhance process efficiency and yield prediction.
• Versatile architecture: The hybrid physics-AI architecture is extendable to various process systems, maintaining scientific credibility.

Technology readiness level

This technology is at TRL 5, indicating that it has been validated in relevant environments, specifically within manufacturing settings. Future validation plans include working closely with partners to integrate and test the digital twin framework in their unique process environments, focusing on real-time data exploration and model calibration, with the aim of demonstrating tangible process improvements.