California-based Palo Alto Research Center (PARC), a Xerox-owned R&D subsidiary, has chosen AlphaSTAR technology to create a virtual additive manufacturing (AM) method that will save some time in 3D printing turbine applications And materials. AlphaSTAR's AM simulation solution GENOA 3DP will be used as one of the projects of the U.S. Department of Energy (DOE) Advanced Research Projects Agency-Energy (ARPA-E) DIFFERENTIATE program.
This differentiation plan, referred to as the arduous energy saving and emission reduction of design intelligence, has countless influential advanced technological improvements from a gender perspective, and aims to strengthen the pace of energy innovation through mergers, artificial intelligence (AI) and machine learning to the development of energy technology .
Since its launch in 2019, the program has successfully raised up to 20 million U.S. dollars in funding and included 22 projects, led by top US research universities, organizations and companies. In particular, a project has been focusing on the development of design optimization tools for turbomachinery components AM based on laser powder bed fusion, which are mainly used for power generation, aircraft and vehicle propulsion.
The research project is titled "Integrated Multiphysics, Design of Additive Components Produced by Turbines". The project started in May 2020 and has received US$1.3 million in funding. It is funded by major partners General Electric (GE) and PARC and Oaktree. Ridge National Laboratory (ORNL) established).
The goal is to reduce the time to design and verify 3D printed components by up to 65%. Achieving such an unprecedented speed will make it faster than some traditional manufacturing processes, paving the way for the wider popularization of AM, thus completely changing the design of turbomachinery products.
PARC hopes to transform the manufacturing industry through the next generation of computer-aided design tools.
By integrating the latest advances in multi-physical topology optimization with rapid production capacity assessment based on machine learning and AI, the team hopes to "break certain traditional manufacturing process schedules by automating the entire process." By reducing the time it takes to create and verify defect-free 3D component designs, this is expected to eventually realize the widespread use and benefits of 3D printing.
The integrated method will be used to prove the productivity and thermodynamic efficiency of multiphysics turbomachinery components while improving. According to the project description, improving the efficiency of turbomachinery is a competitive advantage of the US industry and will help ensure US energy security. The proposed perceivable manufacturing capabilities, multi-physics detailed design optimization tools are expected to help promote the use of AM in the United States.
The new collaboration between PARC and AlphaSTAR aims to create a virtual additive manufacturing method that will save time and materials. AlphaSTAR's predictive simulation technology can help draw temperature maps of part thickness and calculate residual stress, strain, deformation and curvature.
And PARC's topology optimization software can optimize the material layout. The combination of the two allows PARC engineers to quickly adjust the virtual model to improve and make the printed parts lighter, which is critical to creating new opportunities for future applications of turbomachinery structures, especially in the aerospace field.
Saigopal Nelaturi, Research Director of PARC, said: “One of the biggest challenges in metal additive manufacturing design is to ensure that parts are manufactured in a reliable and cost-effective manner.” “GENOA 3DP can help predict and plan factors that affect the manufacturing process, such as residual stress. , Which will help improve the design process of turbomachinery parts. We are happy to work with the AlphaSTAR team to solve practical problems in additive manufacturing design."
AlphaSTAR's GENOA 3DP is designed as a test verification simulation tool that can evaluate and predict shrinkage, warpage and residual stress common in additive manufacturing. Ultimately produce optimized AM parts, and reduce waste and test time. The platform can simulate additive manufacturing materials and process parameters, and evaluate the sensitivity of those parameters to find optimized additive manufacturing solutions.
Although GENOA 3DP was originally developed with thermoplastics in mind, the simulation tool has now been updated to add metal AM simulation capabilities.
AlphaSTAR's GENOA 3DP simulation.
AlphaSTAR has used the platform with research partners and commercial end users to improve new and existing AM designs. According to "Design News", GENOA3DP was recently used in a study that focused on the application of AM technology in the manufacture of prototype wings.
Rashid Miraj, AlphaSTAR’s Director of Technical Operations, suggested: “In terms of innovative solutions, both companies are focusing on the long-term vision.” “We are very happy to work with PARC and its partners to develop this novel program, which can solve the problem with Real industrial needs related to Metal AM."
Once completed, the program will be the final demonstration of a defect-free, high-performance additive manufacturing multifunctional design that can withstand high temperatures and stresses and has better performance than traditional casting.
According to Saigopal Nelaturi, manager of automated computing in the field of systems engineering at PARC Systems Science Laboratory, combining model-based AI with data-driven AI to accelerate generative design is a key innovation that will greatly reduce synthesis and development time. Manufacturing high-quality parts. In addition, the addition of AlphaSTAR AM simulation technology can help speed up the verification time of 3D printing, eliminating one of the biggest obstacles to wider adoption of AM technology.