Secure and Digital Manufacturing at the Oak Ridge National Laboratory’s Manufacturing Demonstration Facility
Join us for a special Lunch & Learn Seminar featuring Vincent C. Paquit, section head of Secure & Digital Manufacturing at Oak Ridge National Laboratory, as he shares groundbreaking advancements in digital engineering, AI, and manufacturing innovation.
Please join us for a special Lunch & Learn Seminar with guest speaker Vincent C. Paquit, section head of Secure & Digital Manufacturing at Oak Ridge National Laboratory.
*Lunch to be provided for in-person attendees on a first come first served basis while supplies last (40 boxed lunches in total).
Registration is preferred but not required. In-person space is limited to around 40 attendees.
Register here.
Abstract: The Secure and Digital Manufacturing Section at Oak Ridge National Laboratory (ORNL) is pioneering advancements in manufacturing at the intersection of digital engineering, software development, systems instrumentation, and automation and control. Our multidisciplinary team is driving innovations that enhance the accuracy, efficiency, and agility of modern manufacturing ecosystems.
This presentation will highlight key research efforts, including the development of secure digital twins, where we will describe our approach to data management in support of our research objectives; multimodal in-situ monitoring demonstrated on large-scale additive manufacturing systems; and AI-driven quality control for powder bed technologies. Finally, we will present an industry case study leveraging AI and model-driven process optimization to produce critical components.
Bio: Dr. Vincent Paquit is the Section Head for Secure & Digital Manufacturing in the Manufacturing Science Division and also serves as the Data Analytics Lead for the Manufacturing Demonstration Facility (MDF), both based at Oak Ridge National Laboratory. Within the MDF, he leads a team of scientists and engineers dedicated to developing a Data Analytics Framework for Advanced Manufacturing. This framework generates digital twins of manufactured components and extensively leverages AI for information extraction. Its primary goal is to enhance the understanding of manufacturing processes, facilitating part certification and qualification while supporting the implementation of process-informed design, inspection, control, and correction.