Secure Online Digital Twin Incorporating Physics-Aware Machine Learning for Additive Manufacturing

The goal of this project is development and deployment of an online digital twin framework that will securely, reliably, and efficiently deliver the best part quality possible in additive manufacturing.


Currently, there isn’t a digital twin framework that allows for real-time interactive performance monitoring and control of manufacturing systems. A big challenge in developing such a digital twin is accelerating computations so they can be done in real-time. (Machine-learning algorithms, with their generalizing capability, are an ideal substitute for compute-intensive manufacturing simulations.) Additionally, real-time control and interconnectivity of machine tools increase the threat of cyberattack, so cybersecurity tools must be incorporated into this framework.

Proposed Solution

The team will develop a real-time, online digital twin to enable closed-loop control of advanced additive manufacturing processes. It will leverage physics-aware machine learning to connect sensor and process parameter output to material properties; high-speed graphics-processing unit (GPU) computing to enable real-time control; and cybersecurity principles to support trust and provenance in manufacturing line security.


With MxD funding, the team will create and deploy a novel digital twin framework for additive manufacturing processes, leading to efficient fabrication of high-value components. This solution will for the first time use a digital twin and machine learning for real-time control in manufacturing and will mitigate cybersecurity threats by incorporating cybersecurity measures into the framework. Ultimately, collaboration with industries will enable wide adoption of the methodology by manufacturers.