Autonomous Additive Manufacturing
Automation and autonomy can enable revolutionary scientific advances by coordinating a diverse array of experimental and computational capabilities more efficiently and more effectively than current hands-on approaches. This project creates an autonomous system to plan and adaptively control additive manufacturing (AM) build processes (Fig. 110). It involves multiple characterization modes, computation across the edge-to-center computing continuum, and multiple scientific user facilities. The objective of the autonomous additive manufacturing (AAM) system is to control the residual stress in a part to address a grand challenge – building parts that are ready and safe to use immediately (i.e., “born qualified”).
Fig. 110 The INTERSECT AAM system performs 3D metal printing with in-situ observations and thermo-mechanical simulations to build “born qualified” structures.
This project enables secure, automated, time-sensitive interactions between experimental and computational components. It demonstrates a new method for autonomous control, combining in-situ observations and thermo-mechanical simulations for accurate real-time state estimation. It uses thermo-mechanical simulations in the control loop to predict the complex, long-range effects of 3D metal printing process parameters on part quality. This autonomous system targets AM builds with residual stress at least two times closer to the desired distribution than current methods, drastically reducing the time to develop process parameters for new alloys and geometries.
The AAM system is deployed at Oak Ridge National Laboratory’s (ORNL’s) Manufacturing Demonstration Facility (MDF), Spallation Neutron Source (SNS), and Oak Ridge Leadership Computing Facility (OLCF) as a cross-facility instrument-science workflow. Its architecture consists of Science Use Case Design Patterns, a System-of-Systems Architecture, and a Microservices Architecture.
- Project Web site:
The Oak Ridge National Laboratory project team consists of: