Xerox’s PARC to Use AlphaSTAR Simulation to 3D Print Turbomachinery Parts

California-based Palo Alto Research Center (PARC), a Xerox-owned research and development subsidiary company, has selected AlphaSTAR technology to create a virtual additive manufacturing (AM) approach that will save both time and materials for 3D printed parts of turbomachinery applications. AlphaSTAR’s AM simulation solution GENOA 3DP will be used as part of one of the projects of the U.S. Department of Energy’s (DOE’s) Advanced Research Projects Agency-Energy (ARPA-E) DIFFERENTIATE program.

The DIFFERENTIATE program, short for Design Intelligence for Formidable Energy Reduction Engendering Numerous Totally Impactful Advanced Technology Enhancements, seeks to reinforce the pace of energy innovation by incorporating artificial intelligence (AI) and machine learning into energy technology developments. Since being launched in 2019, the program has managed to raise up to $20 million in funding and incorporated 22 projects, led by top research U.S. universities, organizations, and businesses. One project, in particular, has been focusing on the development of design optimization tools for laser powder bed fusion-based AM of turbomachinery components, which are mainly used in electrical power generation, aircraft, and vehicular propulsion.

Titled Design of Integrated Multi-physics, Producible Additive Components for Turbomachinery, the research project, which began in May 2020 and has been awarded $1.3 million, teams up leading partner General Electric (GE) along with PARC and the Oak Ridge National Laboratory (ORNL). The aim is to reduce the timeline for designing and validating 3D printed components by as much as 65%. Achieving such unprecedented speeds would make it faster than some traditional manufacturing processes, paving the way for the much broader proliferation of AM to revolutionize turbomachinery product design.

By integrating the latest advances in multi-physics topology optimization with fast machine learning-based producibility evaluations and AI, the team hopes to “beat the timeline for some traditional manufacturing processes by automating the entire process.” This is expected to ultimately enable the widespread use and benefits of 3D printing, by reducing the time it takes to create and validate defect-free 3D component designs.

The integrated methodology will be used to demonstrate simultaneous improvements in the producibility and thermodynamic efficiency of a multi-physics turbomachinery component. According to the project description, improved turbomachinery efficiency is a competitive advantage for U.S. industry and will help ensure the nation’s energy security. The proposed manufacturing producibility-aware, multi-physics detailed design optimization tools are expected to help advance the use of AM within the U.S.

PARC and AlphaSTAR’s new collaboration will aim to create a virtual additive manufacturing approach that will save both time and materials. AlphaSTAR’s predictive simulation technology can help map temperatures through the thickness of the parts, calculating residual stresses, strain, deformations, and curvature. While PARC’s topology optimization software optimizes material layout. The combination of the two allows PARC engineers to quickly tweak virtual models to improve and make printed parts more lightweight, which is critical to creating new opportunities for future applications of turbomachinery structures, especially in the aerospace sector.

“One of the biggest challenges in design for metal additive manufacturing is ensuring that the part can be fabricated in a reliable and cost-effective way,” said Saigopal Nelaturi, Research Director at PARC. “GENOA 3DP can help predict and plan for factors that affect the fabrication process, like residual stresses, which will help improve the design process for turbomachinery parts. We are very excited to collaborate with the AlphaSTAR team to solve real-world problems in design for additive manufacturing.”

Designed as a test validation simulation tool, AlphaSTAR’s GENOA 3DP can assess and predict shrinkage, warpage, and residual stress which are common to AM fabrication. Ultimately resulting in an optimized AM part, as well as reducing waste and testing time. The platform can simulate AM material and process parameters and assess the sensitivities of those parameters to find an optimized AM build solution. While GENOA 3DP was originally developed with thermoplastics in mind, the simulation tool has now been updated to add metal AM simulation capabilities.

AlphaSTAR has used the platform in conjunction with research partners and commercial end-users to improve both new and existing AM designs. Recently, GENOA3DP was used in a study that focused on the application of AM technology to fabricate a prototype wing, reported Design News.

“There is an excellent synergy in the vision of both companies to be on the tip of the spear when it comes to innovative solutions”, suggested Rashid Miraj, Director of Technical Operations at AlphaSTAR. “We are delighted to be working with PARC and their partners on this novel program that addresses the real-world Industrial needs as they relate to Metal AM.”

Once finished, the program 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 versus 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 synthesize and fabricate quality parts. Moreover, the incorporation of AlphaSTAR’s AM simulation technology can help accelerate the time of 3D print to validation, eliminating one of the largest barriers to more widespread adoption of AM technology.