GE, in partnership with Oak Ridge National Laboratory (ORNL), and PARC, a Xerox company, have been awarded more than $1.3 million in funding from ARPA-E’s DIFFERENTIATE (Design Intelligence Fostering Formidable Energy Reduction (and) Enabling Novel Totally Impactful Advanced Technology Enhancements) programme, for a project to reduce the timeline for designing and validating metal additively manufactured components by as much as 65%.
Achieving such speeds would make metal Additive Manufacturing faster than some traditional manufacturing processes, paving the way for the much broader proliferation of the technology in turbomachinery product design.
Today, the design of new components for complex power products such as jet engines, wind turbines and gas turbines involves dozens of experts in the various structural, thermal and fluid properties that apply to them. When designing a new component for AM, a wide variety of factors must be considered such as how well its material composition responds to heat and stresses, or how its design impacts airflow or aerodynamic performance. Pulling this expertise together and later validating a part can take between two and five years.
Researchers from GE, ORNL and PARC explained that they believe they can reduce the overall timeline for creating and validating new AM part designs by more than half, which would make AM faster than traditional casting. Brent Brunell, leader of GE Research’s Additive efforts, stated, “One of the keys to enabling the widespread use and benefits of 3D printing is the reduction of the time it takes to create and validate defect-free 3D component designs. Using multi-physics enabled tools and AI, we think we can beat the timeline for some traditional manufacturing processes by automating the entire process.”
Brunell explained that the optimisation of structural characteristics has already been automated, but has not extended to a part’s thermal and fluid properties. On this project, researchers from GE and PARC will seek to incorporate all three, using AI to automatically generate surrogate models from additive producibility data and seamlessly integrate it with multi-physics design optimisation techniques.
The team will use the Summit supercomputer at the Oak Ridge Leadership Computing Facility at ORNL to create these AI-based surrogates with very high precision. In addition, ORNL’s High Flux Isotope Reactor will be used to analyse additively manufactured components and generate the data necessary for training and evaluating AI-based models.
“This is the type of project that leverages the unique capabilities at ORNL – experimental and computational facilities – as well as expertise in computational science and Additive Manufacturing,” commented John Turner, Computational Engineering Program Director at ORNL.
The programme will culminate in the demonstration of a defect-free, high-performance additively manufactured multi-functional design capable of withstanding high temperatures and stresses with improved performance vs conventional casting. According to Saigopal Nelaturi, Manager of Computation for Automation in Systems Engineering area in the System Sciences Lab at PARC, “The combination of model-based and data-driven AI to accelerate generative design is a key innovation that will dramatically reduce the time to synthesise and fabricate quality parts.
“Surrogate models (built using machine learning) that encapsulate complex couplings between process physics and part quality will help guide the optimisation models in feasible regions of very high dimensional design spaces,” Nelaturi explained. “This combination of AI techniques enables automatic multi-functional part synthesis to meet real-world application demands, for which AM can provide truly novel solutions.”